Glasgow TB ACF
1. Libraries and functions
1.1 Libraries
Load the required libraries.
library(tidyverse)
library(sf)
library(here)
library(readxl)
library(scales)
library(DT)
library(brms)
library(tidybayes)
library(patchwork)
library(marginaleffects)
library(ggrepel)
library(scico)
library(ggdensity)
library(ggpubr)
1.2 Helper functions
Functions that we will use throughout the script
#labeller for years
year_labels <- c(1950:1963)
#The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
#Segment for graphs to match ACF period
acf_start <- decimal_date(ymd("1957-03-11"))
acf_end <- decimal_date(ymd("1957-04-12"))
Function for counterfactual plots
plot_counterfactual <- function(model_data, model, population_denominator, outcome, grouping_var=NULL, ...){
#labeller for years
year_labels <- c(1950:1963)
#The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
#Segment for graphs to match ACF period
acf_start <- decimal_date(ymd("1957-03-11"))
acf_end <- decimal_date(ymd("1957-04-12"))
summary <- {{model_data}} %>%
select(year, year2, y_num, acf_period, {{population_denominator}}, {{outcome}}, {{grouping_var}}) %>%
add_epred_draws({{model}}) %>%
group_by(year2, acf_period, {{grouping_var}}) %>%
mean_qi() %>%
mutate(.epred_inc = .epred/{{population_denominator}}*100000,
.epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
.epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
acf_period=="c. post-acf" ~ "Post Intervention"))
#create the counterfactual (no intervention), and summarise
counterfact <-
add_epred_draws(object = {{model}},
newdata = {{model_data}} %>%
select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, {{outcome}}) %>%
mutate(acf_period = "a. pre-acf")) %>%
group_by(year2, acf_period, {{grouping_var}}) %>%
mean_qi() %>%
mutate(.epred_inc = .epred/{{population_denominator}}*100000,
.epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
.epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
acf_period=="c. post-acf" ~ "Post Intervention"))
#plot the intervention effect
p <- summary %>%
droplevels() %>%
ggplot() +
geom_ribbon(aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, group = acf_period, fill=acf_period), alpha=0.5) +
geom_ribbon(data = counterfact %>% filter(year>=1956),
aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, fill="Counterfactual"), alpha=0.5) +
geom_line(data = counterfact %>% filter(year>=1956),
aes(y=.epred_inc, x=year2, colour="Counterfactual")) +
geom_line(aes(y=.epred_inc, x=year2, group=acf_period, colour=acf_period)) +
geom_point(data = {{model_data}}, aes(y={{outcome}}, x=year2, shape=acf_period), size=2) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
theme_ggdist() +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels) +
scale_fill_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="") +
scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="") +
scale_shape_discrete(name="") +
labs(
x = "Year",
y = "Case notification rate (per 100,000)",
caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
) +
theme(legend.position = "bottom",
panel.border = element_rect(colour = "grey78", fill=NA),
title = element_text(size=14),
axis.text.x = element_text(size=10, angle = 90, hjust=1, vjust=0.5),
legend.text = element_text(size=12)) +
guides(shape="none")
facet_vars <- vars(...)
if (length(facet_vars) != 0) {
p <- p + facet_wrap(facet_vars)
}
p
}
Function for calculating measures of change over time
summarise_change <- function(model_data, model, population_denominator, grouping_var=NULL){
#a. immediate change
nd_immediate <- {{model_data}} %>%
filter(year %in% c(1956:1957)) %>%
select(acf_period, year, y_num, {{population_denominator}}, {{grouping_var}})
#Calcuate incidence per draw, then summarise.
immediate_change <- add_epred_draws({{model}},
newdata=nd_immediate) %>%
mutate(epred_inc100k = .epred/{{population_denominator}}) %>%
group_by(.draw, {{grouping_var}}) %>%
mutate(acf_inc100k_diff = last(epred_inc100k)-first(epred_inc100k),
acf_inc100k_rr = last(epred_inc100k)/first(epred_inc100k)) %>%
ungroup() %>%
group_by({{grouping_var}}) %>%
mean_qi(acf_inc100k_diff, acf_inc100k_rr) %>%
mutate(change = "Immediate change") %>%
ungroup()
#b. post-ACF change
nd_post <- {{model_data}} %>%
filter(year %in% c(1956,1958)) %>%
select(acf_period, year, y_num, {{population_denominator}}, {{grouping_var}})
#Calcuate incidence per draw, then summarise.
post_change <- add_epred_draws({{model}},
newdata=nd_post) %>%
mutate(epred_inc100k = .epred/{{population_denominator}}) %>%
group_by(.draw, {{grouping_var}}) %>%
mutate(acf_inc100k_diff = last(epred_inc100k)-first(epred_inc100k),
acf_inc100k_rr = last(epred_inc100k)/first(epred_inc100k)) %>%
ungroup() %>%
group_by({{grouping_var}}) %>%
mean_qi(acf_inc100k_diff, acf_inc100k_rr) %>%
mutate(change = "Post-ACF change") %>%
ungroup()
#c. change in slope post vs. pre-ACF
slope_change <- {{model_data}} %>%
select(year, year2, y_num, acf_period, {{population_denominator}}, {{grouping_var}}) %>%
filter(year!=1957) %>%
add_epred_draws({{model}}) %>%
mutate(inc_100k = .epred/{{population_denominator}}*100000) %>%
group_by(year, {{grouping_var}}, acf_period, ) %>%
mean_qi(inc_100k) %>%
ungroup() %>%
mutate(n_years = length(year), .by=c(acf_period, {{grouping_var}})) %>%
summarise(pct_change_epred_overall = (((last(inc_100k) - first(inc_100k))/first(inc_100k))),
pct_change_lower_overall = (((last(.lower) - first(.lower))/first(.lower))),
pct_change_upper_overall = (((last(.upper) - first(.upper))/first(.upper))),
pct_change_epred_annual = (((last(inc_100k) - first(inc_100k))/first(inc_100k))/n_years),
pct_change_lower_annual = (((last(.lower) - first(.lower))/first(.lower))/n_years),
pct_change_upper_annual = (((last(.upper) - first(.upper))/first(.upper))/n_years),
.by = c(acf_period, {{grouping_var}})) %>%
distinct() %>%
mutate(change = "Slope change")
lst(immediate_change, post_change, slope_change)
}
Function for calculating difference from counterfactual
calcuate_counterfactual <- function(model_data, model, population_denominator, grouping_var=NULL){
#effect in 1958 vs. counterfactual
counterfact <-
add_epred_draws(object = {{model}},
newdata = {{model_data}} %>%
select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}) %>%
mutate(acf_period = "a. pre-acf")) %>%
group_by(.draw, year, {{grouping_var}}, acf_period) %>%
mutate(.epred_inc_counterf = .epred/{{population_denominator}}*100000, .epred_counterf=.epred) %>%
filter(year>1957) %>%
ungroup() %>%
select(year, {{population_denominator}}, .draw, .epred_counterf, .epred_inc_counterf)
#Calcuate incidence per draw, then summarise.
post_change <-
add_epred_draws(object = {{model}},
newdata = {{model_data}} %>%
select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, acf_period)) %>%
group_by(.draw, year, {{grouping_var}}, acf_period) %>%
mutate(.epred_inc = .epred/{{population_denominator}}*100000) %>%
filter(year>1957) %>%
ungroup() %>%
select(year, {{population_denominator}}, {{grouping_var}}, .draw, .epred, .epred_inc)
counter_post <-
left_join(counterfact, post_change) %>%
mutate(cases_averted = .epred_counterf-.epred,
diff_inc100k = .epred_inc - .epred_inc_counterf,
rr_inc100k = .epred_inc/.epred_inc_counterf,
pct_inc100k = (.epred_inc - .epred_inc_counterf)/.epred_inc) %>%
group_by(year, {{grouping_var}}) %>%
mean_qi(cases_averted, diff_inc100k, rr_inc100k, pct_inc100k) %>%
ungroup()
lst(counter_post)
}
2. Data
Import datasets for analysis
2.1 Shapefiles
glasgow_wards_1951 <- st_read("glasgow_wards_1959.geojson")Reading layer `glasgow_wards_1959' from data source `/Users/petermacpherson/Dropbox/Projects/Historical TB ACF 2023-11-28/Work/Data/glasgow_wards_1959.geojson' using driver `GeoJSON'
Simple feature collection with 37 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 44.2138 ymin: -2611.823 xmax: 3193.173 ymax: -223.6828
Geodetic CRS: WGS 843. Denominators
Load in the datasets for denonomiators, and check for consistency.
overall_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "overall_population")
overall_pops %>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) %>%
datatable()
#shift year to midpoint
overall_pops <- overall_pops %>%
mutate(year2 = year+0.5)
Note, we have three population estimates:
- Population without institutionalised people or people in shipping
- Population in institutions
- Population in shipping
(Population in shipping is estimated from the 1951 census, so is the same for most years)
3.1 Overall population
First, plot the total population
overall_pops %>%
ggplot() +
geom_area(aes(y=total_population, x=year2), alpha=0.5, colour = "mediumseagreen", fill="mediumseagreen") +
geom_point(aes(y=total_population, x=year2), colour = "mediumseagreen") +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels) +
labs(
title = "Glasgow Corporation: total population",
subtitle = "1950 to 1963",
x = "Year",
y = "Population",
caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
) +
theme_ggdist()
NA
NANow the population excluding institutionalised and shipping population
overall_pops %>%
ggplot() +
geom_area(aes(y=population_without_inst_ship, x=year2), alpha=0.5, colour = "purple", fill="purple") +
geom_point(aes(y=population_without_inst_ship, x=year2), colour = "purple") +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels) +
labs(
title = "Glasgow Corporation: population excluding institutionalised and shipping",
subtitle = "1950 to 1963",
x = "Year",
y = "Population",
caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
) +
theme_ggdist()
NA
NA3.2 Population by Ward
There are 5 Divisions containing 37 Wards in the Glasgow Corporation, with consistent boundaries over time.
#look-up table for divisions and wards
ward_lookup <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "divisions_wards")
ward_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "ward_population")
ward_pops %>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) %>%
datatable()
#shift year to midpoint
ward_pops <- ward_pops %>%
mutate(year2 = year+0.5)
#Get the Division population
division_pops <- ward_pops %>%
group_by(division, year) %>%
summarise(population_without_inst_ship = sum(population_without_inst_ship, na.rm = TRUE),
institutions = sum(institutions, na.rm = TRUE),
shipping = sum(shipping, na.rm = TRUE),
total_population = sum(total_population, na.rm = TRUE))`summarise()` has grouped output by 'division'. You can override using the `.groups` argument.division_pops %>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) %>%
datatable()NAPlot the overall population by Division and Ward
division_pops %>%
mutate(year2 = year+0.5) %>%
ggplot() +
geom_area(aes(y=total_population, x=year2, colour=division, fill=division), alpha=0.5) +
geom_point(aes(y=total_population, x=year2, colour=division)) +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
facet_wrap(division~.) +
scale_x_continuous(labels = year_labels,
breaks = year_labels,
guide = guide_axis(angle = 90)) +
scale_fill_viridis_d(name = "") +
scale_colour_viridis_d(name = "") +
labs(
title = "Glasgow Corporation: total population by Division",
subtitle = "1950 to 1963",
x = "Year",
y = "Population",
caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
) +
theme_ggdist() +
theme(legend.position = "bottom")
NA
NA
ward_pops %>%
ggplot() +
geom_area(aes(y=total_population, x=year2, colour=division, fill=division), alpha=0.5) +
geom_point(aes(y=total_population, x=year2, colour=division)) +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
facet_wrap(ward~., ncol=6) +
scale_x_continuous(labels = year_labels,
breaks = year_labels,
guide = guide_axis(angle = 90)) +
scale_fill_viridis_d(name = "Division") +
scale_colour_viridis_d(name = "Division") +
labs(
title = "Glasgow City: total population by Ward",
subtitle = "1950 to 1963",
x = "Year",
y = "Population",
caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
) +
theme_ggdist() +
theme(legend.position = "bottom")
ggsave(here("figures/s1.png"), height=10, width=12)
Approximately, how many person-years of follow-up do we have?
overall_pops %>%
ungroup() %>%
summarise(across(year, length, .names = "years"),
across(c(population_without_inst_ship, total_population), sum)) %>%
mutate(across(where(is.double), comma)) %>%
datatable()NA
NAChange in population by ward
ward_pops %>%
group_by(ward) %>%
summarise(pct_change_pop = (last(population_without_inst_ship) - first(population_without_inst_ship))/first(population_without_inst_ship)) %>%
mutate(pct_change_pop = percent(pct_change_pop)) %>%
arrange(pct_change_pop) %>%
datatable()NA
NA
NA3.3 Population by age and sex
age_sex <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "age_sex_population") %>%
pivot_longer(cols = c(male, female),
names_to = "sex")
#collapse down to smaller age groups to be manageable
age_sex <- age_sex %>%
ungroup() %>%
mutate(age = case_when(age == "0 to 4" ~ "00 to 04",
age == "5 to 9" ~ "05 to 14",
age == "10 to 14" ~ "05 to 14",
age == "15 to 19" ~ "15 to 24",
age == "20 to 24" ~ "15 to 24",
age == "25 to 29" ~ "25 to 34",
age == "30 to 34" ~ "25 to 34",
age == "35 to 39" ~ "35 to 44",
age == "40 to 44" ~ "35 to 44",
age == "45 to 49" ~ "45 to 59",
age == "50 to 54" ~ "45 to 59",
age == "55 to 59" ~ "45 to 59",
TRUE ~ "60 & up")) %>%
group_by(year, age, sex) %>%
mutate(value = sum(value)) %>%
ungroup()
m_age_sex <- lm(value ~ splines::ns(year, knots = 3)*age*sex, data = age_sex)
summary(m_age_sex)Warning: essentially perfect fit: summary may be unreliable
Call:
lm(formula = value ~ splines::ns(year, knots = 3) * age * sex,
data = age_sex)
Residuals:
Min 1Q Median 3Q Max
-1.185e-10 0.000e+00 0.000e+00 0.000e+00 1.185e-10
Coefficients: (14 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.222e+04 2.040e-10 2.559e+14 <2e-16 ***
splines::ns(year, knots = 3)1 -8.043e+03 4.071e-10 -1.976e+13 <2e-16 ***
splines::ns(year, knots = 3)2 NA NA NA NA
age05 to 14 3.669e+04 2.499e-10 1.468e+14 <2e-16 ***
age15 to 24 -3.893e+03 2.499e-10 -1.558e+13 <2e-16 ***
age25 to 34 -3.996e+04 2.499e-10 -1.599e+14 <2e-16 ***
age35 to 44 -4.230e+04 2.499e-10 -1.693e+14 <2e-16 ***
age45 to 59 5.459e+04 2.356e-10 2.317e+14 <2e-16 ***
age60 & up 7.533e+04 2.204e-10 3.418e+14 <2e-16 ***
sexmale 3.374e+03 2.886e-10 1.169e+13 <2e-16 ***
splines::ns(year, knots = 3)1:age05 to 14 -1.863e+03 4.985e-10 -3.737e+12 <2e-16 ***
splines::ns(year, knots = 3)2:age05 to 14 NA NA NA NA
splines::ns(year, knots = 3)1:age15 to 24 7.533e+04 4.985e-10 1.511e+14 <2e-16 ***
splines::ns(year, knots = 3)2:age15 to 24 NA NA NA NA
splines::ns(year, knots = 3)1:age25 to 34 1.325e+05 4.985e-10 2.658e+14 <2e-16 ***
splines::ns(year, knots = 3)2:age25 to 34 NA NA NA NA
splines::ns(year, knots = 3)1:age35 to 44 1.380e+05 4.985e-10 2.769e+14 <2e-16 ***
splines::ns(year, knots = 3)2:age35 to 44 NA NA NA NA
splines::ns(year, knots = 3)1:age45 to 59 3.474e+03 4.700e-10 7.390e+12 <2e-16 ***
splines::ns(year, knots = 3)2:age45 to 59 NA NA NA NA
splines::ns(year, knots = 3)1:age60 & up -8.453e+04 4.397e-10 -1.923e+14 <2e-16 ***
splines::ns(year, knots = 3)2:age60 & up NA NA NA NA
splines::ns(year, knots = 3)1:sexmale -1.994e+03 5.757e-10 -3.464e+12 <2e-16 ***
splines::ns(year, knots = 3)2:sexmale NA NA NA NA
age05 to 14:sexmale 1.053e+04 3.534e-10 2.980e+13 <2e-16 ***
age15 to 24:sexmale 2.352e+04 3.534e-10 6.656e+13 <2e-16 ***
age25 to 34:sexmale 1.355e+04 3.534e-10 3.833e+13 <2e-16 ***
age35 to 44:sexmale -1.727e+03 3.534e-10 -4.888e+12 <2e-16 ***
age45 to 59:sexmale 2.774e+03 3.332e-10 8.324e+12 <2e-16 ***
age60 & up:sexmale -7.761e+04 3.117e-10 -2.490e+14 <2e-16 ***
splines::ns(year, knots = 3)1:age05 to 14:sexmale -2.049e+04 7.051e-10 -2.906e+13 <2e-16 ***
splines::ns(year, knots = 3)2:age05 to 14:sexmale NA NA NA NA
splines::ns(year, knots = 3)1:age15 to 24:sexmale -6.780e+04 7.051e-10 -9.616e+13 <2e-16 ***
splines::ns(year, knots = 3)2:age15 to 24:sexmale NA NA NA NA
splines::ns(year, knots = 3)1:age25 to 34:sexmale -3.804e+04 7.051e-10 -5.396e+13 <2e-16 ***
splines::ns(year, knots = 3)2:age25 to 34:sexmale NA NA NA NA
splines::ns(year, knots = 3)1:age35 to 44:sexmale -1.171e+04 7.051e-10 -1.661e+13 <2e-16 ***
splines::ns(year, knots = 3)2:age35 to 44:sexmale NA NA NA NA
splines::ns(year, knots = 3)1:age45 to 59:sexmale -3.473e+04 6.647e-10 -5.224e+13 <2e-16 ***
splines::ns(year, knots = 3)2:age45 to 59:sexmale NA NA NA NA
splines::ns(year, knots = 3)1:age60 & up:sexmale 1.056e+05 6.218e-10 1.698e+14 <2e-16 ***
splines::ns(year, knots = 3)2:age60 & up:sexmale NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.074e-11 on 44 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 6.006e+29 on 27 and 44 DF, p-value: < 2.2e-16age_levels <- age_sex %>% select(age) %>% distinct() %>% pull()
age_sex_nd <-
crossing(
age=age_levels,
sex=c("male", "female"),
year = 1950:1963
)
pred_pops <- age_sex_nd %>% modelr::add_predictions(m_age_sex)Warning: prediction from a rank-deficient fit may be misleadingpred_pops %>%
ggplot(aes(x=year, y=pred, colour=age)) +
geom_line() +
geom_point() +
facet_grid(sex~.) +
scale_y_continuous(labels = comma, limits = c(0, 125000))
#How well do they match up with our overall populations?
pred_pops %>%
group_by(year) %>%
summarise(sum_pred_pop = sum(pred)) %>%
right_join(overall_pops) %>%
select(year, sum_pred_pop, population_without_inst_ship, total_population) %>%
pivot_longer(cols = c(sum_pred_pop, population_without_inst_ship, total_population)) %>%
ggplot(aes(x=year, y=value, colour=name)) +
geom_point() +
scale_y_continuous(labels = comma, limits = c(800000, 1250000))Joining with `by = join_by(year)`
pred_pops %>%
group_by(year, sex) %>%
summarise(sum = sum(pred)) %>%
group_by(year) %>%
mutate(sex_ratio = first(sum)/last(sum))`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.Population pyramids
label_abs <- function(x) {
comma(abs(x))
}
pred_pops %>%
ungroup() %>%
group_by(year) %>%
mutate(year_pop = sum(pred),
age_sex_pct = percent(pred/year_pop, accuracy=0.1)) %>%
mutate(sex = case_when(sex=="male" ~ "Male",
sex=="female" ~ "Female")) %>%
ggplot(
aes(x = age, fill = sex,
y = ifelse(test = sex == "Female",yes = -pred, no = pred))) +
geom_bar(stat = "identity") +
geom_text(aes(label = age_sex_pct),
position= position_stack(vjust=0.5), colour="white", size=2.5) +
facet_wrap(year~., ncol=7) +
coord_flip() +
scale_y_continuous(labels = label_abs) +
scale_fill_manual(values = c("mediumseagreen", "purple"), name="") +
theme_ggdist() +
theme(axis.text.x = element_text(angle=90, hjust = 1, vjust=0.5),
legend.position = "bottom",
panel.border = element_rect(colour = "grey78", fill=NA)) +
labs(x="", y="")
ggsave(here("figures/s2.png"), width=10)Saving 10 x 4.51 in image
Not perfect, but resonably good. But ahhhhh… the age groups don’t align with the case notification age groups! Come back to think about this later.
4. Tuberculosis cases
Import the tuberculosis cases dataset
4.1 Overall notifications
Overall, by year.
cases_by_year <- read_xlsx("2023-11-28_glasgow-acf.xlsx", sheet = "by_year")
cases_by_year%>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) %>%
datatable()
#shift year to midpoint
cases_by_year <- cases_by_year %>%
mutate(year2 = year+0.5)
Plot the overall number of case notified per year, by pulmonary and extra pulmonary classification.
cases_by_year %>%
select(-total_notifications, -year) %>%
pivot_longer(cols = c(pulmonary_notifications, `non-pulmonary_notifications`)) %>%
mutate(name = case_when(name == "pulmonary_notifications" ~ "Pulmonary TB",
name == "non-pulmonary_notifications" ~ "Extra-pulmonary TB")) %>%
ggplot() +
geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels) +
scale_fill_brewer(palette = "Set1", name="") +
labs(
title = "Glasgow Corporation: Tuberculosis notifications",
subtitle = "1950 to 1963, by TB disease classification",
x = "Year",
y = "Number of cases",
caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
) +
theme_ggdist() +
theme(legend.position = "bottom")
NA
NA4.2 Notifications by Division
Read in the datasets and merge together.
#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")
#get the ward sheets
ward_sheets <- enframe(all_sheets) %>%
filter(grepl("by_ward", value)) %>%
pull(value)
cases_by_ward_sex_year <- map_df(ward_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
sheet = .))
cases_by_ward_sex_year %>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) %>%
datatable()NAAggregate together to case cases by division
cases_by_division <- cases_by_ward_sex_year %>%
left_join(ward_lookup) %>%
group_by(division, year, tb_type) %>%
summarise(cases = sum(cases, na.rm = TRUE))Joining with `by = join_by(ward)``summarise()` has grouped output by 'division', 'year'. You can override using the `.groups` argument.#shift year to midpoint
cases_by_division <- cases_by_division %>%
mutate(year2 = year+0.5) %>%
ungroup()
cases_by_division %>%
select(-year2) %>%
select(year, everything()) %>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) %>%
datatable()
cases_by_division %>%
mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
ggplot() +
geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels,
guide = guide_axis(angle = 90)) +
facet_wrap(division~., scales = "free_y") +
scale_fill_brewer(palette = "Set1", name="") +
labs(
title = "Glasgow Corporation: Tuberculosis notifications by Division",
subtitle = "1950 to 1963, by TB disease classification",
x = "Year",
y = "Number of cases",
caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
) +
theme_ggdist() +
theme(legend.position = "bottom")
4.3 Notifications by ward
cases_by_ward <- cases_by_ward_sex_year %>%
group_by(ward, year, tb_type) %>%
summarise(cases = sum(cases, na.rm = TRUE)) %>%
ungroup()`summarise()` has grouped output by 'ward', 'year'. You can override using the `.groups` argument.cases_by_ward %>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) %>%
select(year, everything()) %>%
datatable()
#shift year to midpoint
cases_by_ward <- cases_by_ward %>%
mutate(year2 = year+0.5)
cases_by_ward %>%
mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
ggplot() +
geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.8) +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels,
guide = guide_axis(angle = 90)) +
facet_wrap(ward~., scales = "free_y") +
scale_fill_brewer(palette = "Set1", name="") +
labs(
title = "Glasgow Corporation: Tuberculosis notifications by Ward",
subtitle = "1950 to 1963, by TB disease classification",
x = "Year",
y = "Number of cases",
caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
) +
theme(legend.position = "bottom")
NA
NA4.4 Notifications by age and sex
As we don’t have denominators, we will just model the change in counts.
#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")
#get the ward sheets
age_sex_sheets <- enframe(all_sheets) %>%
filter(grepl("by_age_sex", value)) %>%
pull(value)
cases_by_age_sex <- map_df(age_sex_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
sheet = .))
cases_by_age_sex %>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) %>%
datatable()NA
NA5 TB incidence
5.1 Overall TB incidence
Now calculate incidence per 100,000 population
Merge the notification and population denominator datasets together.
Here we need to include the whole population (with shipping and institutions) as they are included in the notifications.
overall_inc <- overall_pops %>%
left_join(cases_by_year)Joining with `by = join_by(year, year2)`overall_inc <- overall_inc %>%
mutate(inc_pulm_100k = pulmonary_notifications/total_population*100000,
inc_ep_100k = `non-pulmonary_notifications`/total_population*100000,
inc_100k = total_notifications/total_population*100000)
overall_inc %>%
select(year, inc_100k, inc_pulm_100k, inc_ep_100k) %>%
mutate_at(.vars = vars(inc_100k, inc_pulm_100k, inc_ep_100k),
.funs = funs(round)) %>%
datatable()Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
overall_inc %>%
select(year2, inc_pulm_100k, inc_ep_100k) %>%
pivot_longer(cols = c(inc_pulm_100k, `inc_ep_100k`)) %>%
mutate(name = case_when(name == "inc_pulm_100k" ~ "Pulmonary TB",
name == "inc_ep_100k" ~ "Extra-pulmonary TB")) %>%
ggplot() +
geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels) +
scale_fill_brewer(palette = "Set1", name="") +
labs(
title = "Glasgow Corporation: Tuberculosis case notification rate",
subtitle = "1950 to 1963, by TB disease classification",
x = "Year",
y = "Case notification rate (per 100,000)",
caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
) +
theme_ggdist() +
theme(legend.position = "bottom")
NA
NA
NA5.2 TB incidence by Division
division_inc <- division_pops %>%
left_join(cases_by_division)Joining with `by = join_by(division, year)`division_inc <- division_inc %>%
mutate(inc_100k = cases/total_population*100000)
division_inc %>%
select(year, division, tb_type, inc_100k) %>%
mutate_at(.vars = vars(inc_100k),
.funs = funs(round)) %>%
datatable()Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
division_inc %>%
mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
ggplot() +
geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels,
guide = guide_axis(angle = 90)) +
facet_wrap(division~.) +
scale_fill_brewer(palette = "Set1", name="") +
labs(
title = "Glasgow Corporation: Tuberculosis case notification rate, by Division",
subtitle = "1950 to 1963, by TB disease classification",
x = "Year",
y = "Case notification rate (per 100,000)",
caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
) +
theme_ggdist() +
theme(legend.position = "bottom")
NA
NA
NA5.2 TB incidence by Ward
Here we will filter out the institutions and harbour from the denominators, as we don’t have reliable population denominators for them.
ward_inc <- ward_pops %>%
left_join(cases_by_ward)Joining with `by = join_by(ward, year, year2)`ward_inc <- ward_inc %>%
mutate(inc_100k = cases/population_without_inst_ship*100000)
ward_inc %>%
select(year, ward, tb_type, inc_100k) %>%
mutate_at(.vars = vars(inc_100k),
.funs = funs(round)) %>%
datatable()Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
ward_inc %>%
mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
ggplot() +
geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels,
guide = guide_axis(angle = 90)) +
facet_wrap(ward~.) +
scale_fill_brewer(palette = "Set1", name="") +
labs(
title = "Glasgow Corporation: Tuberculosis case notification rate, by Ward",
subtitle = "1950 to 1963, by TB disease classification",
x = "Year",
y = "Incidence (per 100,000)",
caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
) +
theme(legend.position = "bottom")
NA
NA
NA
NAOn a map
st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
filter(tb_type=="Pulmonary") %>%
ggplot() +
geom_sf(aes(fill=inc_100k)) +
facet_wrap(year~., ncol = 7) +
scale_fill_viridis_c(name="Case notification rate (per 100,000)",
option = "A") +
theme_ggdist() +
theme(legend.position = "top",
legend.key.width = unit(2, "cm"),
panel.border = element_rect(colour = "grey78", fill=NA)) +
guides(fill=guide_colorbar(title.position = "top"))Joining with `by = join_by(division, ward, ward_number)`
6. TB Mortality
6.1 Overall Mortality
Import the TB mortality data.
First, overall deaths. Note that in the original reports, we have a pulmonary TB death rate per million for all years, and numbers of pulmonary TB deaths for each year apart from 1950.
#get the overall mortality sheets
deaths_sheets <- enframe(all_sheets) %>%
filter(grepl("deaths", value)) %>%
pull(value)
overall_deaths <- map_df(deaths_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
sheet = .))
overall_deaths %>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) %>%
datatable()NA
NA
NAPlot the raw numbers of pulmonary deaths
overall_deaths %>%
ggplot(aes(x=year, y=pulmonary_deaths)) +
geom_line(colour = "#DE0D92") +
geom_point(colour = "#DE0D92") +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
labs(y="Pulmonary TB deaths per year",
x = "Year",
title = "Numbers of pulmonary TB deaths",
subtitle = "Glasgow, 1950-1963",
caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: no data for 1950") +
theme_ggdist() +
theme(panel.border = element_rect(colour = "grey78", fill=NA))
NA
NANow the incidence of pulmonary TB death
overall_deaths %>%
ggplot(aes(x=year, y=pulmonary_death_rate_per_100k)) +
geom_line(colour = "#4D6CFA") +
geom_point(colour = "#4D6CFA") +
geom_vline(aes(xintercept=acf_start), linetype=3) +
geom_vline(aes(xintercept=acf_end), linetype=3) +
scale_y_continuous(labels=comma) +
scale_x_continuous(labels = year_labels,
breaks = year_labels) +
labs(y="Annual incidence of death (per 100,000)",
x = "Year",
caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)") +
theme_ggdist() +
theme(panel.border = element_rect(colour = "grey78", fill=NA))
ggsave(here("figures/s7.png"), width=10)Saving 10 x 4.51 in image
6. Table 1
Make Table 1 here, and save for publication.
overall_pops %>%
select(year, total_population) %>%
left_join(overall_inc %>%
select(year,
pulmonary_notifications, inc_pulm_100k,
`non-pulmonary_notifications`, inc_ep_100k,
total_notifications, inc_100k)) %>%
left_join(overall_deaths %>%
select(year,
pulmonary_deaths, pulmonary_death_rate_per_100k)) %>%
mutate(across(where(is.numeric) & !(year), ~round(., digits=1))) %>%
mutate(across(where(is.numeric) & !(year), ~comma(.))) Joining with `by = join_by(year)`Joining with `by = join_by(year)`7. Overall pulmonary TB model
7.1 FIt the model and priors
First model will investigate the impact of mass miniature X-ray campaign on pulmonary TB case notification rate using an interrupted time series analysis.
Set up the data
mdata1 <- overall_inc %>%
mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
year %in% c(1957) ~ "b. acf",
year %in% c(1958:1963) ~ "c. post-acf")) %>%
mutate(y_num = 1:nrow(.)) %>%
rename(extrapulmonary_notifications = `non-pulmonary_notifications`)
Work on the priors a bit
Basic prior
basic_prior <- c(prior(normal(0, 1), class = Intercept),
prior(normal(0, 0.25), class = b))Look at the mean and variance of counts (counts of pulmonary notifications are what we are predicting)
#Mean of counts per year
mean(mdata1$pulmonary_notifications)[1] 1858.429#variance of counts per year
var(mdata1$pulmonary_notifications)[1] 716579.8Quite a bit of over-dispersion here, so negative binomial distribution might be a better choice of distributional family than Poisson.
Slightly more informative prior (“weakly informative” really)
ggplot(data = tibble(x = seq(from = 500, to = 5000, by = 10)),
aes(x = x, y = dgamma(x, shape = 0.1, rate = 0.00001))) +
geom_area(color = "transparent",
fill = "#DE0D92") +
scale_x_continuous(NULL) +
coord_cartesian(xlim = c(500, 5000)) +
ggtitle(expression(brms~~gamma(0.1*", "*0.00001)~shape~prior))
NA
NAFit a model with only priors
winform_prior <- c(prior(normal(0, 1), class = Intercept),
prior(gamma(0.1, 0.00001), class = shape),
prior(normal(0, 0.25), class = b))
m_pulmonary_prior <- brm(
pulmonary_notifications ~ y_num + acf_period + acf_period:y_num + offset(log(total_population)),
data = mdata1,
family = negbinomial(),
seed = 1234,
chains = 4, cores = 4,
prior = winform_prior,
sample_prior = "only",
backend="cmdstanr",
save_pars = save_pars(all = TRUE),
warmup = 1000)Compiling Stan program...
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: public boost::hash_detail::hash_base<T*>
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boost::hash<T> hasher;
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Total execution time: 0.9 seconds.Warning: 86 of 4000 (2.0%) transitions ended with a divergence.
See https://mc-stan.org/misc/warnings for details.summary(m_pulmonary_prior)Warning: There were 86 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup Family: negbinomial
Links: mu = log; shape = identity
Formula: pulmonary_notifications ~ y_num + acf_period + acf_period:y_num + offset(log(total_population))
Data: mdata1 (Number of observations: 14)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept -0.05 2.48 -5.09 4.62 1.00 2538 2031
y_num 0.00 0.25 -0.50 0.48 1.00 2589 2402
acf_periodb.acf 0.00 0.25 -0.49 0.48 1.00 2908 1869
acf_periodc.postMacf 0.01 0.25 -0.48 0.49 1.00 2668 2293
y_num:acf_periodb.acf -0.00 0.25 -0.49 0.46 1.00 2714 2401
y_num:acf_periodc.postMacf 0.00 0.26 -0.51 0.54 1.00 2137 1486
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape 11729.33 36453.14 0.00 104278.62 1.01 937 801
Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).conditional_effects(m_pulmonary_prior)
Now fit the model with the weakly informative priors
m_pulmonary_overall <- brm(
pulmonary_notifications ~ y_num + acf_period + acf_period:y_num + offset(log(total_population)),
data = mdata1,
family = negbinomial(),
seed = 1234,
chains = 4, cores = 4,
prior = winform_prior,
save_pars = save_pars(all = TRUE),
backend = "cmdstanr",
warmup = 1000)Compiling Stan program...
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Total execution time: 1.0 seconds.summary(m_pulmonary_overall) Family: negbinomial
Links: mu = log; shape = identity
Formula: pulmonary_notifications ~ y_num + acf_period + acf_period:y_num + offset(log(total_population))
Data: mdata1 (Number of observations: 14)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept -6.10 0.03 -6.17 -6.04 1.00 2999 2234
y_num -0.02 0.01 -0.03 -0.01 1.00 2904 2266
acf_periodb.acf 0.02 0.25 -0.45 0.53 1.00 1764 2081
acf_periodc.postMacf 0.05 0.11 -0.19 0.26 1.00 1935 1892
y_num:acf_periodb.acf 0.08 0.03 0.01 0.14 1.00 1755 2160
y_num:acf_periodc.postMacf -0.05 0.01 -0.08 -0.03 1.00 1821 1648
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape 4409.62 15136.27 322.80 26935.74 1.00 1318 1213
Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).plot(m_pulmonary_overall)
pp_check(m_pulmonary_overall, type='ecdf_overlay')Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
7.2 Summarise change in CNRs
Summarise the posterior
f1b <- plot_counterfactual(model_data = mdata1, model = m_pulmonary_overall, population_denominator = total_population, outcome = inc_pulm_100k, grouping_var=NULL)
f1b
Make this into a figure
f1a <- st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
filter(tb_type=="Pulmonary") %>%
ggplot() +
geom_sf(aes(fill=inc_100k)) +
facet_wrap(year~., ncol = 7) +
scale_fill_viridis_c(name="Case notification rate (per 100,000)",
option = "A") +
theme_ggdist() +
theme(panel.border = element_rect(colour = "grey78", fill=NA),
legend.position = "top",
#legend.key.width = unit(2, "cm"),
legend.title.align = 0.5) +
guides(fill=guide_colorbar(title.position = "top"))Joining with `by = join_by(division, ward, ward_number)`(f1a / f1b) + plot_annotation(tag_levels = "A")
ggsave(here("figures/f1.png"))Saving 7.29 x 4.51 in image
Summary of change in notifications
summarise_change(model_data=mdata1, model=m_pulmonary_overall, population_denominator=total_population, grouping_var=NULL) %>%
map(datatable)Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in dplyr 1.1.0.
Please use `reframe()` instead.
When switching from `summarise()` to `reframe()`, remember that `reframe()` always returns an ungrouped data frame and adjust accordingly.$immediate_change
$post_change
$slope_changeNA(Alternative way - keep in for now)
overall_immediate_draws <- mdata1 %>%
select(year, year2, y_num, acf_period, total_population, pulmonary_notifications) %>%
filter(year %in% c(1956,1957)) %>%
add_epred_draws(m_pulmonary_overall) %>%
mutate(inc_100k = .epred/total_population*100000) %>%
group_by(.draw) %>%
summarise(pct_change_immediate = (last(inc_100k) - first(inc_100k))/first(inc_100k)) %>%
ungroup()
overall_post_draws <- mdata1 %>%
select(year, year2, y_num, acf_period, total_population, pulmonary_notifications) %>%
filter(year %in% c(1956,1958)) %>%
add_epred_draws(m_pulmonary_overall) %>%
mutate(inc_100k = .epred/total_population*100000) %>%
group_by(.draw) %>%
summarise(pct_change_post = (last(inc_100k) - first(inc_100k))/first(inc_100k)) %>%
ungroup()
overall_slope_draws <- mdata1 %>%
select(year, year2, y_num, acf_period, total_population, pulmonary_notifications) %>%
filter(year %in% c(1950, 1956, 1958, 1963)) %>%
add_epred_draws(m_pulmonary_overall) %>%
mutate(inc_100k = .epred/total_population*100000) %>%
ungroup() %>%
mutate(n_years = length(year), .by=acf_period) %>%
group_by(acf_period, .draw) %>%
summarise(pct_change_slope = ((last(inc_100k) - first(inc_100k))/first(inc_100k))/n_years) %>%
distinct() %>%
pivot_wider(names_from = c(acf_period),
values_from = pct_change_slope) %>%
mutate(ratio_annual_slope = `c. post-acf` / `a. pre-acf`)Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in dplyr 1.1.0.
Please use `reframe()` instead.
When switching from `summarise()` to `reframe()`, remember that `reframe()` always returns an ungrouped data frame and adjust accordingly.`summarise()` has grouped output by 'acf_period', '.draw'. You can override using the `.groups` argument.Correlation between immediate effect and post effect of ACF
left_join(overall_immediate_draws, overall_post_draws) %>%
ggplot(aes(x=pct_change_immediate, y=pct_change_post)) +
geom_hdr(
aes(fill = after_stat(probs)),
alpha = 1) +
#geom_hdr_points(aes(colour = after_stat(probs)), size=0.5) +
geom_smooth(method = "lm", se=FALSE, colour="black", linewidth=0.5) +
stat_regline_equation(label.x = 0.25, label.y = -0.25, size=4) +
scale_x_continuous(labels = percent,
breaks = pretty_breaks(n = 10)) +
scale_y_continuous(labels = percent,
breaks = pretty_breaks(n = 5)) +
scale_fill_viridis_d(option="E", name="") +
labs(title="Correlation between immediate ACF impact and post-ACF case notification rate",
y="Post intervention impact: ercentage change in CNR (1958 vs. 1956)",
x="Immediate impact: percentage change in CNR (1957 vs. 1956)",
caption="Boundaries are posterior desnity intervals from 4000 draws") +
theme_ggdist() +
theme(legend.position = "bottom",
panel.border = element_rect(colour = "grey78", fill=NA))Joining with `by = join_by(.draw)`
Correlation between immediate effect and change in slope
left_join(overall_immediate_draws, overall_slope_draws) %>%
ggplot(aes(x=pct_change_immediate, y=ratio_annual_slope)) +
geom_hdr(
aes(fill = after_stat(probs)),
alpha = 1) +
#geom_hdr_points(aes(colour = after_stat(probs)), size=0.5) +
geom_smooth(method = "lm", se=FALSE, colour="black", linewidth=0.5) +
#stat_regline_equation(label.x = 0.25, label.y = 0.02, size=4) +
scale_x_continuous(labels = percent,
breaks = pretty_breaks(n = 10)) +
scale_y_continuous(breaks = pretty_breaks(n = 5),
limits = c(0, 10)) +
scale_fill_viridis_d(option="E", name="") +
labs(title="Correlation between immediate ACF impact and post-ACF case notification rate",
y="Post intervention impact: Percentage change in CNR (1958 vs. 1956)",
x="Immediate impact: percentage change in CNR (1957 vs. 1956)",
caption="Points are draws from posteior distribution") +
theme_ggdist() +
theme(legend.position = "bottom",
panel.border = element_rect(colour = "grey78", fill=NA))Joining with `by = join_by(.draw)`
Another way
as_draws_df(m_pulmonary_overall)# A draws_df: 1000 iterations, 4 chains, and 10 variables# ... with 3990 more draws, and 2 more variables
# ... hidden reserved variables {'.chain', '.iteration', '.draw'}7.3 Compared to counterfactual
overall_pulmonary_counterf <- calcuate_counterfactual(model_data = mdata1, model=m_pulmonary_overall, population_denominator = total_population)Joining with `by = join_by(year, total_population, .draw)`overall_pulmonary_counterf %>%
map(datatable)$counter_postNATotal pulmonary TB cases averted between 1958 and 1963
overall_pulmonary_counterf$counter_post %>%
summarise(across(c(cases_averted, cases_averted.lower, cases_averted.upper), sum))NA
NA8. Extra-pulmonary TB notifications
8.1 Fit the
m_extrap_overall <- brm(
extrapulmonary_notifications ~ y_num + acf_period + acf_period:y_num +offset(log(total_population)),
data = mdata1,
family = poisson(),
seed = 1234,
chains = 4, cores = 4,
prior = basic_prior,
save_pars = save_pars(all = TRUE),
backend = "cmdstanr",
warmup = 1000)
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Mean chain execution time: 0.2 seconds.
Total execution time: 0.9 seconds.summary(m_extrap_overall) Family: poisson
Links: mu = log
Formula: extrapulmonary_notifications ~ y_num + acf_period + acf_period:y_num + offset(log(total_population))
Data: mdata1 (Number of observations: 14)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept -7.90 0.05 -7.99 -7.81 1.00 2908 2532
y_num -0.09 0.01 -0.11 -0.07 1.00 2623 2183
acf_periodb.acf -0.00 0.24 -0.48 0.48 1.00 2558 2487
acf_periodc.postMacf -0.32 0.17 -0.64 0.01 1.00 2095 2415
y_num:acf_periodb.acf -0.02 0.03 -0.08 0.04 1.00 2413 2577
y_num:acf_periodc.postMacf 0.02 0.02 -0.02 0.05 1.00 1711 2108
Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).plot(m_extrap_overall)
pp_check(m_extrap_overall, type='ecdf_overlay')
plot_counterfactual(model_data = mdata1, model=m_extrap_overall, population_denominator = total_population, outcome=inc_ep_100k)
ggsave(here("figures/s7.png"), width=10)Saving 10 x 4.51 in image
8.2 Summary of change
A. Percentage change in mortality, from 1956 to 1957 (i.e. immediate ACF effect)
summarise_change(model_data=mdata1, model = m_extrap_overall, population_denominator = total_population) %>%
map(datatable)Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in dplyr 1.1.0.
Please use `reframe()` instead.
When switching from `summarise()` to `reframe()`, remember that `reframe()` always returns an ungrouped data frame and adjust accordingly.$immediate_change
$post_change
$slope_changeNA8.3 Compared to counterfactual
overall_ep_counterf <- calcuate_counterfactual(model_data = mdata1, model=m_extrap_overall, population_denominator = total_population)Joining with `by = join_by(year, total_population, .draw)`overall_ep_counterf %>%
map(datatable)$counter_postNATotal extra pulmonary TB cases averted between 1958 and 1963
overall_ep_counterf $counter_post %>%
summarise(across(c(cases_averted, cases_averted.lower, cases_averted.upper), sum))NA
NA9. Ward level model
9.1 Fit the model
mdata2 <- ward_inc %>%
filter(tb_type=="Pulmonary") %>%
mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
year %in% c(1957) ~ "b. acf",
year %in% c(1958:1963) ~ "c. post-acf")) %>%
group_by(ward) %>%
mutate(y_num = row_number()) %>%
ungroup()
(Note the denominator without institutionalised people and “shipping”!)
#weakly informative priors for multilevel model
basic_prior2 <- c(prior(normal(0, 1), class = Intercept),
prior(normal(0, 0.1), class = b),
prior(cauchy(0,5), class="sd"))
ggplot(data = tibble(x = seq(from = 0, to = 250, by = 10)),
aes(x = x, y = dgamma(x, shape = 0.5, rate = 0.01))) +
geom_area(color = "transparent",
fill = "#DE0D92") +
scale_x_continuous(NULL) +
scale_y_continuous(NULL, breaks = NULL) +
coord_cartesian(xlim = c(0, 250)) +
ggtitle(expression(brms~~gamma(0.5*", "*0.00001)~shape~prior))
winform_prior2 <- c(prior(normal(0, 1), class = Intercept),
prior(gamma(0.5, 0.01), class = shape),
prior(normal(0, 1), class = b),
prior(cauchy(0,5), class="sd"),
prior(lkj(2), class="cor"))
m_pulmonary_ward_prior <- brm(
cases ~ y_num + acf_period + acf_period:y_num + (1 + y_num + acf_period + acf_period:y_num | ward) + offset(log(population_without_inst_ship)),
data = mdata2,
family = negbinomial(),
seed = 1234,
chains = 4, cores = 4,
prior = winform_prior2,
save_pars = save_pars(all = TRUE),
sample_prior = "only",
backend = "cmdstanr",
warmup = 1000)Compiling Stan program...
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Total execution time: 1.4 seconds.conditional_effects(m_pulmonary_ward_prior)
NA
NANow fit the model with data
m_pulmonary_ward <- brm(
cases ~ y_num + acf_period + acf_period:y_num + (1 + y_num + acf_period + acf_period:y_num | ward) + offset(log(population_without_inst_ship)),
data = mdata2,
family = negbinomial(),
seed = 1234,
chains = 4, cores = 4,
prior = winform_prior2,
backend = "cmdstanr")Compiling Stan program...
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hash_combine(seed, &v.category());
^
|/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__functional/unary_function.h:23:29: note: 'unary_function<const std::error_category *, unsigned long>' has been explicitly marked deprecated here
struct _LIBCPP_TEMPLATE_VIS _LIBCPP_DEPRECATED_IN_CXX11 unary_function
^
/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:850:41: note: expanded from macro '_LIBCPP_DEPRECATED_IN_CXX11'
# define _LIBCPP_DEPRECATED_IN_CXX11 _LIBCPP_DEPRECATED
^
/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:835:49: note: expanded from macro '_LIBCPP_DEPRECATED'
# define _LIBCPP_DEPRECATED __attribute__((deprecated))
^
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Mean chain execution time: 79.0 seconds.
Total execution time: 80.9 seconds.summary(m_pulmonary_ward) Family: negbinomial
Links: mu = log; shape = identity
Formula: cases ~ y_num + acf_period + acf_period:y_num + (1 + y_num + acf_period + acf_period:y_num | ward) + offset(log(population_without_inst_ship))
Data: mdata2 (Number of observations: 518)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Group-Level Effects:
~ward (Number of levels: 37)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.26 0.04 0.20 0.34 1.00 1698 2316
sd(y_num) 0.02 0.01 0.01 0.03 1.01 1217 978
sd(acf_periodb.acf) 0.08 0.05 0.00 0.18 1.00 1573 2064
sd(acf_periodc.postMacf) 0.13 0.08 0.01 0.30 1.00 939 1471
sd(y_num:acf_periodb.acf) 0.01 0.01 0.00 0.02 1.00 1612 2176
sd(y_num:acf_periodc.postMacf) 0.01 0.01 0.00 0.03 1.01 576 1121
cor(Intercept,y_num) -0.51 0.20 -0.81 -0.04 1.00 2566 2373
cor(Intercept,acf_periodb.acf) -0.28 0.33 -0.80 0.43 1.00 2987 2293
cor(y_num,acf_periodb.acf) -0.06 0.32 -0.64 0.58 1.00 5536 3410
cor(Intercept,acf_periodc.postMacf) -0.18 0.28 -0.67 0.41 1.00 4468 3115
cor(y_num,acf_periodc.postMacf) 0.14 0.30 -0.45 0.70 1.00 3192 3324
cor(acf_periodb.acf,acf_periodc.postMacf) 0.10 0.32 -0.54 0.69 1.00 1909 2968
cor(Intercept,y_num:acf_periodb.acf) -0.27 0.33 -0.79 0.45 1.00 3493 2858
cor(y_num,y_num:acf_periodb.acf) -0.07 0.32 -0.66 0.57 1.00 5401 3217
cor(acf_periodb.acf,y_num:acf_periodb.acf) -0.09 0.34 -0.71 0.56 1.00 3930 3316
cor(acf_periodc.postMacf,y_num:acf_periodb.acf) 0.07 0.33 -0.58 0.67 1.00 3674 3348
cor(Intercept,y_num:acf_periodc.postMacf) 0.02 0.31 -0.58 0.61 1.00 4641 2858
cor(y_num,y_num:acf_periodc.postMacf) -0.10 0.33 -0.69 0.57 1.00 1930 2364
cor(acf_periodb.acf,y_num:acf_periodc.postMacf) 0.07 0.33 -0.58 0.65 1.00 2736 3112
cor(acf_periodc.postMacf,y_num:acf_periodc.postMacf) -0.16 0.36 -0.81 0.55 1.00 1594 1728
cor(y_num:acf_periodb.acf,y_num:acf_periodc.postMacf) 0.07 0.33 -0.57 0.68 1.00 1848 2837
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept -6.14 0.05 -6.24 -6.04 1.00 1155 1961
y_num -0.02 0.01 -0.03 -0.01 1.00 3100 3310
acf_periodb.acf 0.02 1.02 -1.99 1.98 1.00 4602 3119
acf_periodc.postMacf 0.05 0.11 -0.16 0.25 1.00 4476 2977
y_num:acf_periodb.acf 0.08 0.13 -0.16 0.33 1.00 4578 3148
y_num:acf_periodc.postMacf -0.05 0.01 -0.07 -0.03 1.00 4216 3135
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape 98.15 23.53 63.84 158.13 1.00 3107 2818
Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).plot(m_pulmonary_ward)
pp_check(m_pulmonary_ward, type='ecdf_overlay')Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
plot_counterfactual(model_data = mdata2, model=m_pulmonary_ward, outcome = inc_100k, population_denominator = population_without_inst_ship, grouping_var = ward, ward)
ggsave(here("figures/s3.png"), width=10, height=12)
9.2 Summary of change
A. percentage increase in CNR, from 1956 to 1957 (i.e. immediate ACF effect)
ward_change <- summarise_change(model_data = mdata2, model = m_pulmonary_ward, population_denominator = population_without_inst_ship, grouping_var=ward) Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in dplyr 1.1.0.
Please use `reframe()` instead.
When switching from `summarise()` to `reframe()`, remember that `reframe()` always returns an ungrouped data frame and adjust accordingly.ward_change %>%
map(datatable)$immediate_change
$post_change
$slope_changeNAAs a supplementary figure
ward_change$immediate_change %>%
arrange(acf_inc100k_rr) %>%
ggplot() +
geom_pointrange(aes(y=acf_inc100k_rr, ymin=acf_inc100k_rr.lower, ymax=acf_inc100k_rr.upper,
x=fct_reorder(ward, acf_inc100k_rr),
colour = acf_inc100k_rr)) +
geom_hline(aes(yintercept=1), linetype=2) +
coord_flip() +
scale_colour_viridis_b(option = "D") +
scale_y_continuous(limits = c(0.8,3.0)) +
labs(x="",
y="Relative posterior predicted case notification rate (per 100,000; 95% UI)\nACF (1957) vs. Before ACF (1956)") +
theme_ggdist() +
theme(legend.position = "none",
panel.border = element_rect(colour = "grey78", fill=NA))
ggsave(here("figures/s4.png"))Saving 7.29 x 4.51 in image
ward_change$post_change %>%
arrange(acf_inc100k_rr) %>%
ggplot() +
geom_pointrange(aes(y=acf_inc100k_rr, ymin=acf_inc100k_rr.lower, ymax=acf_inc100k_rr.upper,
x=fct_reorder(ward, acf_inc100k_rr),
colour = acf_inc100k_rr)) +
geom_hline(aes(yintercept=1), linetype=2) +
coord_flip() +
scale_colour_viridis_b(option = "D") +
#scale_y_continuous(limits = c(0.8,3.0)) +
labs(x="",
y="Relative posterior predicted case notification rate (per 100,000; 95% UI)\nAfter ACF (1958) vs. Before ACF (1956)") +
theme_ggdist() +
theme(legend.position = "none",
panel.border = element_rect(colour = "grey78", fill=NA))
ggsave(here("figures/s5.png"))Saving 7.29 x 4.51 in image
percentage change = (final value - initial value) / initial value
ward_change$slope_change %>%
ggplot() +
geom_pointrange(aes(y=pct_change_epred_overall , ymin=pct_change_lower_overall , ymax=pct_change_upper_overall ,
group=acf_period, colour=acf_period,
x = fct_reorder(ward, pct_change_epred_overall ))) +
coord_flip() +
scale_y_continuous(labels =percent) +
scale_colour_manual(values = c("#DE0D92", "#4D6CFA")) +
#scale_y_continuous(limits = c(0.8,3.0)) +
labs(title="Intervention effect of mass miniature chest X-ray in Glasgow",
subtitle="By municipal ward",
x="",
y="Mean annual rate of change in case notification rate (95% CrI)\n Before ACF (1950-1956) vs. after ACF (1958-1963)") +
theme_ggdist() +
theme(panel.border = element_rect(colour = "grey78", fill=NA))
(Alternative figure - keep in for the minute)
Is there any correlation between immediate increase and a) post-intervention (1958) effect, and b) post intervention slope (1958-1963)
ward_cors <- ward_impact_out %>%
select(ward, immediate_effect = acf_inc100k_rr,
immediate_effect_lower = acf_inc100k_rr.lower,
immediate_effect_upper = acf_inc100k_rr.upper) %>%
right_join(
ward_pulm_impact2 %>%
select(ward, post_effect = acf_inc100k_rr,
post_effect_lower = acf_inc100k_rr.lower,
post_effect_upper = acf_inc100k_rr.upper)
) %>%
right_join(
ward_pulm_impact3 %>%
filter(acf_period=="c. post-acf") %>%
select(ward, slope_effect = pct_change_epred_annual,
slope_effect_lower = pct_change_lower_annual,
slope_effect_upper = pct_change_upper_annual)
)Error in select(., ward, immediate_effect = acf_inc100k_rr, immediate_effect_lower = acf_inc100k_rr.lower, :
object 'ward_impact_out' not foundTry a different way with the full distribution of posteriors
Correlation between immediate effect and post effect of ACF
Correlation between immediate effect and change in slope
Join these together with the overall estimates to make a single figure for showing impact
9.3 Compared to counterfactual
ward_counterf %>%
map(datatable)
$counter_postNATotal pulmonary TB cases averted between 1958 and 1963
10. Age-sex model
Fit the model
(Not rewritten the functions for this yet)
Summarise posterior
Summary of impact of intervention
- percentage increase in CNR, from 1956 to 1957 (i.e. immediate ACF effect)
- Change from pre-ACF period (1956), to first year post-ACF (1958)
- Change in slope (i.e. difference in mean annual case notification rate pre-Intervention vs. post-intervention, by ward)
Join together for Figure 2.
Is there any correlation between immediate increase and a) post-intervention (1958) effect, and b) post intervention slope (1958-1963)
11. Division-level model
(Very much a work in progress!)
---
title: "Glasgow TB ACF"
output: html_notebook
---

### 1. Libraries and functions

#### 1.1 Libraries

Load the required libraries.

```{r, message=F, warning=F}
library(tidyverse)
library(sf)
library(here)
library(readxl)
library(scales)
library(DT)
library(brms)
library(tidybayes)
library(patchwork)
library(marginaleffects)
library(ggrepel)
library(scico)
library(ggdensity)
library(ggpubr)

```

#### 1.2 Helper functions

Functions that we will use throughout the script

```{r}
#labeller for years
year_labels <- c(1950:1963)

#The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
#Segment for graphs to match ACF period
acf_start <- decimal_date(ymd("1957-03-11"))
acf_end <- decimal_date(ymd("1957-04-12"))


```

Function for counterfactual plots

```{r}

plot_counterfactual <- function(model_data, model, population_denominator, outcome, grouping_var=NULL, ...){
  
  #labeller for years
  year_labels <- c(1950:1963)

  #The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
  #Segment for graphs to match ACF period
  acf_start <- decimal_date(ymd("1957-03-11"))
  acf_end <- decimal_date(ymd("1957-04-12"))

  summary <- {{model_data}} %>%
    select(year, year2, y_num, acf_period, {{population_denominator}}, {{outcome}}, {{grouping_var}}) %>%
    add_epred_draws({{model}}) %>%
    group_by(year2, acf_period, {{grouping_var}}) %>%
    mean_qi() %>%
    mutate(.epred_inc = .epred/{{population_denominator}}*100000,
          .epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
          .epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
    mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                  acf_period=="c. post-acf" ~ "Post Intervention"))



  #create the counterfactual (no intervention), and summarise
  
  counterfact <-
    add_epred_draws(object = {{model}},
                    newdata = {{model_data}} %>%
                                  select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, {{outcome}}) %>%
                                  mutate(acf_period = "a. pre-acf")) %>%
    group_by(year2, acf_period, {{grouping_var}}) %>%
    mean_qi() %>%
    mutate(.epred_inc = .epred/{{population_denominator}}*100000,
         .epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
         .epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
    mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention"))
  


  #plot the intervention effect
p <- summary %>%
    droplevels() %>%
    ggplot() +
    geom_ribbon(aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, group = acf_period, fill=acf_period), alpha=0.5) +
    geom_ribbon(data = counterfact %>% filter(year>=1956), 
                aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, fill="Counterfactual"), alpha=0.5) +
    geom_line(data = counterfact %>% filter(year>=1956), 
              aes(y=.epred_inc, x=year2, colour="Counterfactual")) +
    geom_line(aes(y=.epred_inc, x=year2, group=acf_period,  colour=acf_period)) +
    geom_point(data = {{model_data}}, aes(y={{outcome}}, x=year2, shape=acf_period), size=2) +
    geom_vline(aes(xintercept=acf_end), linetype=3) +
    theme_ggdist() +
    scale_y_continuous(labels=comma) +
    scale_x_continuous(labels = year_labels,
                       breaks = year_labels) +
    scale_fill_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="") +
    scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="") +
    scale_shape_discrete(name="") +
    labs(
      x = "Year",
      y = "Case notification rate (per 100,000)",
      caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
    ) +
    theme(legend.position = "bottom",
          panel.border = element_rect(colour = "grey78", fill=NA),
          title = element_text(size=14),
          axis.text.x = element_text(size=10, angle = 90, hjust=1, vjust=0.5),
          legend.text = element_text(size=12)) +
    guides(shape="none")

    facet_vars <- vars(...)

  if (length(facet_vars) != 0) {
    p <- p + facet_wrap(facet_vars)
  }
  p

}

```

Function for calculating  measures of change over time

```{r}

summarise_change <- function(model_data, model, population_denominator, grouping_var=NULL){

  #a. immediate change
  nd_immediate <- {{model_data}} %>%
    filter(year %in% c(1956:1957)) %>%
    select(acf_period, year, y_num, {{population_denominator}}, {{grouping_var}})

  #Calcuate incidence per draw, then summarise.
  immediate_change <- add_epred_draws({{model}},
                                      newdata=nd_immediate) %>%
    mutate(epred_inc100k = .epred/{{population_denominator}}) %>%
    group_by(.draw, {{grouping_var}}) %>%
    mutate(acf_inc100k_diff = last(epred_inc100k)-first(epred_inc100k),
           acf_inc100k_rr = last(epred_inc100k)/first(epred_inc100k)) %>%
    ungroup() %>%
    group_by({{grouping_var}}) %>%
    mean_qi(acf_inc100k_diff, acf_inc100k_rr) %>%
    mutate(change = "Immediate change") %>%
    ungroup()
  
  #b. post-ACF change
  nd_post <- {{model_data}} %>%
    filter(year %in% c(1956,1958)) %>%
    select(acf_period, year, y_num, {{population_denominator}}, {{grouping_var}})

  #Calcuate incidence per draw, then summarise.
  post_change <- add_epred_draws({{model}},
                                      newdata=nd_post) %>%
    mutate(epred_inc100k = .epred/{{population_denominator}}) %>%
    group_by(.draw, {{grouping_var}}) %>%
    mutate(acf_inc100k_diff = last(epred_inc100k)-first(epred_inc100k),
           acf_inc100k_rr = last(epred_inc100k)/first(epred_inc100k)) %>%
    ungroup() %>%
    group_by({{grouping_var}}) %>%
    mean_qi(acf_inc100k_diff, acf_inc100k_rr) %>%
    mutate(change = "Post-ACF change") %>%
    ungroup()
  
  #c. change in slope post vs. pre-ACF
  slope_change <- {{model_data}} %>%
    select(year, year2, y_num, acf_period, {{population_denominator}}, {{grouping_var}}) %>%
    filter(year!=1957) %>%
    add_epred_draws({{model}}) %>%
    mutate(inc_100k = .epred/{{population_denominator}}*100000) %>%
    group_by(year, {{grouping_var}}, acf_period, ) %>%
    mean_qi(inc_100k) %>%
    ungroup() %>%
    mutate(n_years = length(year), .by=c(acf_period, {{grouping_var}})) %>%
    summarise(pct_change_epred_overall = (((last(inc_100k) - first(inc_100k))/first(inc_100k))),
              pct_change_lower_overall = (((last(.lower) - first(.lower))/first(.lower))),
              pct_change_upper_overall = (((last(.upper) - first(.upper))/first(.upper))),
      
              pct_change_epred_annual = (((last(inc_100k) - first(inc_100k))/first(inc_100k))/n_years),
              pct_change_lower_annual = (((last(.lower) - first(.lower))/first(.lower))/n_years),
              pct_change_upper_annual = (((last(.upper) - first(.upper))/first(.upper))/n_years),
              .by = c(acf_period, {{grouping_var}})) %>%
    distinct() %>%
    mutate(change = "Slope change")

  lst(immediate_change, post_change, slope_change)
    
}

```


Function for calculating difference from counterfactual

```{r}
calcuate_counterfactual <- function(model_data, model, population_denominator, grouping_var=NULL){
  
  #effect in 1958 vs. counterfactual
  counterfact <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}) %>%
                                    mutate(acf_period = "a. pre-acf")) %>%
      group_by(.draw, year, {{grouping_var}}, acf_period) %>%
      mutate(.epred_inc_counterf = .epred/{{population_denominator}}*100000, .epred_counterf=.epred)  %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, {{population_denominator}}, .draw, .epred_counterf, .epred_inc_counterf)
  
  #Calcuate incidence per draw, then summarise.
  post_change <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, acf_period)) %>%
      group_by(.draw, year, {{grouping_var}}, acf_period) %>%
      mutate(.epred_inc = .epred/{{population_denominator}}*100000)  %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, {{population_denominator}}, {{grouping_var}}, .draw, .epred, .epred_inc) 
  
counter_post <-
  left_join(counterfact, post_change) %>%
    mutate(cases_averted = .epred_counterf-.epred,
          diff_inc100k = .epred_inc - .epred_inc_counterf,
           rr_inc100k = .epred_inc/.epred_inc_counterf,
           pct_inc100k = (.epred_inc - .epred_inc_counterf)/.epred_inc) %>%
    group_by(year, {{grouping_var}}) %>%
    mean_qi(cases_averted, diff_inc100k, rr_inc100k, pct_inc100k) %>%
    ungroup()

lst(counter_post)

}



```




### 2. Data

Import datasets for analysis

#### 2.1 Shapefiles



```{r}

glasgow_wards_1951 <- st_read("glasgow_wards_1959.geojson")

```

### 3. Denominators

Load in the datasets for denonomiators, and check for consistency.

```{r}

overall_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "overall_population")

overall_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

#shift year to midpoint
overall_pops <- overall_pops %>%
  mutate(year2 = year+0.5)

```

Note, we have three population estimates:

1. Population without institutionalised people or people in shipping
2. Population in institutions
3. Population in shipping

(Population in shipping is estimated from the 1951 census, so is the same for most years)

#### 3.1 Overall population

First, plot the total population

```{r}

overall_pops %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2), alpha=0.5, colour = "mediumseagreen", fill="mediumseagreen") +
  geom_point(aes(y=total_population, x=year2), colour = "mediumseagreen") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(
    title = "Glasgow Corporation: total population",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist()


```

Now the population excluding institutionalised and shipping population

```{r}

overall_pops %>%
  ggplot() +
  geom_area(aes(y=population_without_inst_ship, x=year2), alpha=0.5, colour = "purple", fill="purple") +
  geom_point(aes(y=population_without_inst_ship, x=year2), colour = "purple") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(
    title = "Glasgow Corporation: population excluding institutionalised and shipping",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist()


```

#### 3.2 Population by Ward

There are 5 Divisions containing 37 Wards in the Glasgow Corporation, with consistent boundaries over time.

```{r}
#look-up table for divisions and wards
ward_lookup <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "divisions_wards")


ward_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "ward_population")

ward_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

#shift year to midpoint
ward_pops <- ward_pops %>%
  mutate(year2 = year+0.5)

#Get the Division population
division_pops <- ward_pops %>%
  group_by(division, year) %>%
  summarise(population_without_inst_ship = sum(population_without_inst_ship, na.rm = TRUE),
            institutions = sum(institutions, na.rm = TRUE),
            shipping = sum(shipping, na.rm = TRUE),
            total_population = sum(total_population, na.rm = TRUE))

division_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

```

Plot the overall population by Division and Ward

```{r}

division_pops %>%
  mutate(year2 = year+0.5) %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2, colour=division, fill=division), alpha=0.5) +
  geom_point(aes(y=total_population, x=year2, colour=division)) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  facet_wrap(division~.) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_viridis_d(name = "") +
  scale_colour_viridis_d(name = "") +
  labs(
    title = "Glasgow Corporation: total population by Division",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")


```

```{r}

ward_pops %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2, colour=division, fill=division), alpha=0.5) +
  geom_point(aes(y=total_population, x=year2, colour=division)) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  facet_wrap(ward~., ncol=6) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_viridis_d(name = "Division") +
  scale_colour_viridis_d(name = "Division") +
  labs(
    title = "Glasgow City: total population by Ward",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

ggsave(here("figures/s1.png"), height=10, width=12)

```

Approximately, how many person-years of follow-up do we have?

```{r}

overall_pops %>%
  ungroup() %>%
  summarise(across(year, length, .names = "years"),
            across(c(population_without_inst_ship, total_population), sum)) %>%
  mutate(across(where(is.double), comma)) %>%
  datatable()


```

Change in population by ward

```{r}

ward_pops %>%
  group_by(ward) %>%
  summarise(pct_change_pop = (last(population_without_inst_ship) - first(population_without_inst_ship))/first(population_without_inst_ship)) %>%
  mutate(pct_change_pop = percent(pct_change_pop)) %>%
  arrange(pct_change_pop) %>%
  datatable()
  


```


#### 3.3 Population by age and sex

```{r}

age_sex <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "age_sex_population") %>%
  pivot_longer(cols = c(male, female),
               names_to = "sex")

#collapse down to smaller age groups to be manageable
age_sex <- age_sex %>%
  ungroup() %>%
  mutate(age = case_when(age == "0 to 4" ~ "00 to 04",
                         age == "5 to 9" ~ "05 to 14",
                         age == "10 to 14" ~ "05 to 14",
                         age == "15 to 19" ~ "15 to 24",
                         age == "20 to 24" ~ "15 to 24",
                         age == "25 to 29" ~ "25 to 34",
                         age == "30 to 34" ~ "25 to 34",
                         age == "35 to 39" ~ "35 to 44",
                         age == "40 to 44" ~ "35 to 44",
                         age == "45 to 49" ~ "45 to 59",
                         age == "50 to 54" ~ "45 to 59",
                         age == "55 to 59" ~ "45 to 59",
                         TRUE ~ "60 & up")) %>%
  group_by(year, age, sex) %>%
  mutate(value = sum(value)) %>%
  ungroup()



m_age_sex <- lm(value ~ splines::ns(year, knots = 3)*age*sex, data = age_sex)

summary(m_age_sex)

age_levels <- age_sex %>% select(age) %>% distinct() %>% pull() 

age_sex_nd <- 
  crossing(
    age=age_levels,
    sex=c("male", "female"),
    year = 1950:1963
  )

pred_pops <- age_sex_nd %>% modelr::add_predictions(m_age_sex)

pred_pops %>%
  ggplot(aes(x=year, y=pred, colour=age)) +
  geom_line() +
  geom_point() +
  facet_grid(sex~.) +
  scale_y_continuous(labels = comma, limits = c(0, 125000))

#How well do they match up with our overall populations?
pred_pops %>%
  group_by(year) %>%
  summarise(sum_pred_pop = sum(pred)) %>%
  right_join(overall_pops) %>%
  select(year, sum_pred_pop, population_without_inst_ship, total_population) %>%
  pivot_longer(cols = c(sum_pred_pop, population_without_inst_ship, total_population)) %>%
  ggplot(aes(x=year, y=value, colour=name)) +
  geom_point() +
  scale_y_continuous(labels = comma, limits = c(800000, 1250000))

pred_pops %>%
  group_by(year, sex) %>%
  summarise(sum = sum(pred)) %>%
  group_by(year) %>%
  mutate(sex_ratio = first(sum)/last(sum))
```

Population pyramids

```{r}

label_abs <- function(x) {
  comma(abs(x))
}


pred_pops %>%
  ungroup() %>%
  group_by(year) %>%
  mutate(year_pop = sum(pred),
         age_sex_pct = percent(pred/year_pop, accuracy=0.1)) %>%
  mutate(sex = case_when(sex=="male" ~ "Male",
                         sex=="female" ~ "Female")) %>%
  ggplot(
    aes(x = age, fill = sex, 
        y = ifelse(test = sex == "Female",yes = -pred, no = pred))) + 
  geom_bar(stat = "identity") +
  geom_text(aes(label = age_sex_pct),
            position= position_stack(vjust=0.5), colour="white", size=2.5) +
  facet_wrap(year~., ncol=7) +
  coord_flip() +
  scale_y_continuous(labels = label_abs) +
  scale_fill_manual(values = c("mediumseagreen", "purple"), name="") +
  theme_ggdist() +
  theme(axis.text.x = element_text(angle=90, hjust = 1, vjust=0.5),
        legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="", y="") 


ggsave(here("figures/s2.png"), width=10)


```

Not perfect, but resonably good. But ahhhhh... the age groups don't align with the case notification age groups! Come back to think about this later.


### 4. Tuberculosis cases

Import the tuberculosis cases dataset


#### 4.1 Overall notifications

Overall, by year.

```{r}

cases_by_year <- read_xlsx("2023-11-28_glasgow-acf.xlsx", sheet = "by_year")

cases_by_year%>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()


#shift year to midpoint
cases_by_year <- cases_by_year %>%
  mutate(year2 = year+0.5)

```

Plot the overall number of case notified per year, by pulmonary and extra pulmonary classification.

```{r}

cases_by_year %>%
  select(-total_notifications, -year) %>%
  pivot_longer(cols = c(pulmonary_notifications, `non-pulmonary_notifications`)) %>%
  mutate(name = case_when(name == "pulmonary_notifications" ~ "Pulmonary TB",
                          name == "non-pulmonary_notifications" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")
  

```

#### 4.2 Notifications by Division

Read in the datasets and merge together.

```{r}

#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")

#get the ward sheets
ward_sheets <- enframe(all_sheets) %>%
  filter(grepl("by_ward", value)) %>%
  pull(value)


cases_by_ward_sex_year <- map_df(ward_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

cases_by_ward_sex_year %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

```

Aggregate together to case cases by division

```{r}

cases_by_division <- cases_by_ward_sex_year %>%
  left_join(ward_lookup) %>%
  group_by(division, year, tb_type) %>%
  summarise(cases = sum(cases, na.rm = TRUE))

#shift year to midpoint
cases_by_division <- cases_by_division %>%
  mutate(year2 = year+0.5) %>%
  ungroup()

cases_by_division  %>%
  select(-year2) %>%
  select(year, everything()) %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()


cases_by_division %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(division~., scales = "free_y") +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications by Division",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

```

#### 4.3 Notifications by ward

```{r}


cases_by_ward <- cases_by_ward_sex_year %>%
  group_by(ward, year, tb_type) %>%
  summarise(cases = sum(cases, na.rm = TRUE)) %>%
  ungroup()

cases_by_ward %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  select(year, everything()) %>%
  datatable()

#shift year to midpoint
cases_by_ward <- cases_by_ward %>%
  mutate(year2 = year+0.5)

cases_by_ward %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.8) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(ward~., scales = "free_y") +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications by Ward",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme(legend.position = "bottom")


```

#### 4.4 Notifications by age and sex

As we don't have denominators, we will just model the change in counts.

```{r}

#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")

#get the ward sheets
age_sex_sheets <- enframe(all_sheets) %>%
  filter(grepl("by_age_sex", value)) %>%
  pull(value)


cases_by_age_sex <- map_df(age_sex_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

cases_by_age_sex %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()


```




### 5 TB incidence

#### 5.1 Overall TB incidence

Now calculate incidence per 100,000 population

Merge the notification and population denominator datasets together.

Here we need to include the whole population (with shipping and institutions) as they are included in the notifications.

```{r}

overall_inc <- overall_pops %>%
  left_join(cases_by_year)

overall_inc <- overall_inc %>%
  mutate(inc_pulm_100k = pulmonary_notifications/total_population*100000,
         inc_ep_100k = `non-pulmonary_notifications`/total_population*100000,
         inc_100k = total_notifications/total_population*100000)

overall_inc %>%
  select(year, inc_100k, inc_pulm_100k, inc_ep_100k) %>%
  mutate_at(.vars = vars(inc_100k, inc_pulm_100k, inc_ep_100k),
            .funs = funs(round)) %>%
  datatable()

```

```{r}

overall_inc %>%
  select(year2, inc_pulm_100k, inc_ep_100k) %>%
  pivot_longer(cols = c(inc_pulm_100k, `inc_ep_100k`)) %>%
  mutate(name = case_when(name == "inc_pulm_100k" ~ "Pulmonary TB",
                          name == "inc_ep_100k" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Case notification rate (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")



```

#### 5.2 TB incidence by Division

```{r}

division_inc <- division_pops %>%
  left_join(cases_by_division)


division_inc <- division_inc %>%
  mutate(inc_100k = cases/total_population*100000)

division_inc %>%
  select(year, division, tb_type, inc_100k) %>%
  mutate_at(.vars = vars(inc_100k),
            .funs = funs(round)) %>%
  datatable()


```

```{r}

division_inc %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(division~.) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate, by Division",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Case notification rate (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")



```

#### 5.2 TB incidence by Ward

Here we will filter out the institutions and harbour from the denominators, as we don't have reliable population denominators for them.

```{r}

ward_inc <- ward_pops %>%
  left_join(cases_by_ward)


ward_inc <- ward_inc %>%
  mutate(inc_100k = cases/population_without_inst_ship*100000)

ward_inc %>%
  select(year, ward, tb_type, inc_100k) %>%
  mutate_at(.vars = vars(inc_100k),
            .funs = funs(round)) %>%
  datatable()


```


```{r}

ward_inc %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(ward~.) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate, by Ward",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Incidence (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme(legend.position = "bottom")




```

On a map

```{r, warning=FALSE}

st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
  filter(tb_type=="Pulmonary") %>%
  ggplot() +
  geom_sf(aes(fill=inc_100k)) +
  facet_wrap(year~., ncol = 7) +
  scale_fill_viridis_c(name="Case notification rate (per 100,000)",
                       option = "A") +
  theme_ggdist() +
  theme(legend.position = "top",
        legend.key.width = unit(2, "cm"),
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  guides(fill=guide_colorbar(title.position = "top"))



```


### 6. TB Mortality

#### 6.1 Overall Mortality

Import the TB mortality data.

First, overall deaths. Note that in the original reports, we have a pulmonary TB death rate per million for all years, and numbers of pulmonary TB deaths for each year apart from 1950.

```{r}

#get the overall mortality sheets
deaths_sheets <- enframe(all_sheets) %>%
  filter(grepl("deaths", value)) %>%
  pull(value)


overall_deaths <- map_df(deaths_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

overall_deaths %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()



```

Plot the raw numbers of pulmonary deaths

```{r}

overall_deaths %>%
  ggplot(aes(x=year, y=pulmonary_deaths)) +
  geom_line(colour = "#DE0D92") +
  geom_point(colour = "#DE0D92") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  labs(y="Pulmonary TB deaths per year",
       x = "Year",
       title = "Numbers of pulmonary TB deaths",
       subtitle = "Glasgow, 1950-1963",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: no data for 1950") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))


```

Now the incidence of pulmonary TB death

```{r}
overall_deaths %>%
  ggplot(aes(x=year, y=pulmonary_death_rate_per_100k)) +
  geom_line(colour = "#4D6CFA") +
  geom_point(colour = "#4D6CFA") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(y="Annual incidence of death (per 100,000)",
       x = "Year",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))

ggsave(here("figures/s7.png"), width=10)

```


### 6. Table 1

Make Table 1 here, and save for publication.

```{r}

overall_pops %>% 
  select(year, total_population) %>%
  left_join(overall_inc %>%
              select(year, 
                     pulmonary_notifications, inc_pulm_100k,
                     `non-pulmonary_notifications`, inc_ep_100k,
                     total_notifications, inc_100k)) %>%
  left_join(overall_deaths %>%
              select(year,
                     pulmonary_deaths, pulmonary_death_rate_per_100k)) %>%
  mutate(across(where(is.numeric) & !(year),  ~round(., digits=1))) %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) 

```




### 7. Overall pulmonary TB model


#### 7.1 FIt the model and priors

First model will investigate the impact of mass miniature X-ray campaign on pulmonary TB case notification rate using an interrupted time series analysis.

Set up the data

```{r}

mdata1 <- overall_inc %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  mutate(y_num = 1:nrow(.)) %>%
  rename(extrapulmonary_notifications = `non-pulmonary_notifications`)

```


Work on the priors a bit

Basic prior

```{r}

basic_prior <- c(prior(normal(0, 1), class = Intercept),
                 prior(normal(0, 0.25), class = b))
```

Look at the mean and variance of counts (counts of pulmonary notifications are what we are predicting)

```{r}

#Mean of counts per year
mean(mdata1$pulmonary_notifications)
#variance of counts per year
var(mdata1$pulmonary_notifications)

```


Quite a bit of over-dispersion here, so negative binomial distribution might be a better choice of distributional family than Poisson.

Slightly more informative prior ("weakly informative" really)

```{r}

ggplot(data = tibble(x = seq(from = 500, to = 5000, by = 10)),
       aes(x = x, y = dgamma(x, shape = 0.1, rate = 0.00001))) +
  geom_area(color = "transparent", 
            fill = "#DE0D92") +
  scale_x_continuous(NULL) +
  coord_cartesian(xlim = c(500, 5000)) +
  ggtitle(expression(brms~~gamma(0.1*", "*0.00001)~shape~prior))


```

Fit a model with only priors

```{r}

winform_prior <- c(prior(normal(0, 1), class = Intercept),
                  prior(gamma(0.1, 0.00001), class = shape),
                  prior(normal(0, 0.25), class = b))


m_pulmonary_prior <- brm(
  pulmonary_notifications ~ y_num + acf_period + acf_period:y_num +  offset(log(total_population)),
                  data = mdata1,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4, 
                  prior = winform_prior,
                  sample_prior = "only",
                  backend="cmdstanr",
                  save_pars = save_pars(all = TRUE),
                  warmup = 1000)

summary(m_pulmonary_prior)
conditional_effects(m_pulmonary_prior)

```

Now fit the model with the weakly informative priors


```{r}
m_pulmonary_overall <- brm(
  pulmonary_notifications ~ y_num + acf_period + acf_period:y_num +  offset(log(total_population)),
                  data = mdata1,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4, 
                  prior = winform_prior,
                  save_pars = save_pars(all = TRUE),
                  backend = "cmdstanr",
                  warmup = 1000)

summary(m_pulmonary_overall)
plot(m_pulmonary_overall)
pp_check(m_pulmonary_overall, type='ecdf_overlay')

```

#### 7.2 Summarise change in CNRs

Summarise the posterior

```{r}

f1b <- plot_counterfactual(model_data = mdata1, model = m_pulmonary_overall, population_denominator = total_population, outcome = inc_pulm_100k, grouping_var=NULL)
  
f1b
```

Make this into a figure

```{r}

f1a <- st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
  filter(tb_type=="Pulmonary") %>%
  ggplot() +
  geom_sf(aes(fill=inc_100k)) +
  facet_wrap(year~., ncol = 7) +
  scale_fill_viridis_c(name="Case notification rate (per 100,000)",
                       option = "A") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA),
        legend.position = "top",
        #legend.key.width = unit(2, "cm"),
        legend.title.align = 0.5) +
  guides(fill=guide_colorbar(title.position = "top"))

(f1a / f1b) + plot_annotation(tag_levels = "A")

ggsave(here("figures/f1.png"))

```


Summary of change in notifications

```{r}

summarise_change(model_data=mdata1, model=m_pulmonary_overall, population_denominator=total_population, grouping_var=NULL) %>%
  map(datatable)

```
(Alternative way - keep in for now)

```{r}

overall_immediate_draws <- mdata1 %>%
  select(year, year2, y_num, acf_period, total_population, pulmonary_notifications) %>%
  filter(year %in% c(1956,1957)) %>%
  add_epred_draws(m_pulmonary_overall) %>%
  mutate(inc_100k = .epred/total_population*100000) %>%
  group_by(.draw) %>%
  summarise(pct_change_immediate = (last(inc_100k) - first(inc_100k))/first(inc_100k)) %>%
  ungroup()

overall_post_draws <- mdata1 %>%
  select(year, year2, y_num, acf_period, total_population, pulmonary_notifications) %>%
  filter(year %in% c(1956,1958)) %>%
  add_epred_draws(m_pulmonary_overall) %>%
  mutate(inc_100k = .epred/total_population*100000) %>%
  group_by(.draw) %>%
  summarise(pct_change_post = (last(inc_100k) - first(inc_100k))/first(inc_100k)) %>%
  ungroup()


overall_slope_draws <- mdata1 %>%
  select(year, year2, y_num, acf_period, total_population, pulmonary_notifications) %>%
  filter(year %in% c(1950, 1956, 1958, 1963)) %>%
  add_epred_draws(m_pulmonary_overall) %>%
  mutate(inc_100k = .epred/total_population*100000) %>%
  ungroup() %>%
  mutate(n_years = length(year), .by=acf_period) %>%
  group_by(acf_period, .draw) %>%
  summarise(pct_change_slope = ((last(inc_100k) - first(inc_100k))/first(inc_100k))/n_years) %>%
  distinct() %>%
  pivot_wider(names_from = c(acf_period),
              values_from = pct_change_slope) %>%
  mutate(ratio_annual_slope = `c. post-acf` / `a. pre-acf`)


```

Correlation between immediate effect and post effect of ACF

```{r}

left_join(overall_immediate_draws, overall_post_draws) %>%
  ggplot(aes(x=pct_change_immediate, y=pct_change_post)) +
  geom_hdr(
    aes(fill = after_stat(probs)), 
    alpha = 1) +
  #geom_hdr_points(aes(colour = after_stat(probs)), size=0.5) +
  geom_smooth(method = "lm", se=FALSE, colour="black", linewidth=0.5) +
  stat_regline_equation(label.x = 0.25, label.y = -0.25, size=4) +
  scale_x_continuous(labels = percent,
                     breaks = pretty_breaks(n = 10)) +
  scale_y_continuous(labels = percent,
                     breaks = pretty_breaks(n = 5)) +
  scale_fill_viridis_d(option="E", name="") +
  labs(title="Correlation between immediate ACF impact and post-ACF case notification rate",
       y="Post intervention impact: ercentage change in CNR (1958 vs. 1956)",
       x="Immediate impact: percentage change in CNR (1957 vs. 1956)",
       caption="Boundaries are posterior desnity intervals from 4000 draws") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))


```

Correlation between immediate effect and change in slope

```{r}

left_join(overall_immediate_draws, overall_slope_draws) %>%
  ggplot(aes(x=pct_change_immediate, y=ratio_annual_slope)) +
  geom_hdr(
    aes(fill = after_stat(probs)), 
    alpha = 1) +
  #geom_hdr_points(aes(colour = after_stat(probs)), size=0.5) +
  geom_smooth(method = "lm", se=FALSE, colour="black", linewidth=0.5) +
  #stat_regline_equation(label.x = 0.25, label.y = 0.02, size=4) +
  scale_x_continuous(labels = percent,
                     breaks = pretty_breaks(n = 10)) +
  scale_y_continuous(breaks = pretty_breaks(n = 5),
                     limits = c(0, 10)) +
  scale_fill_viridis_d(option="E", name="") +
  labs(title="Correlation between immediate ACF impact and post-ACF case notification rate",
       y="Post intervention impact: Percentage change in CNR (1958 vs. 1956)",
       x="Immediate impact: percentage change in CNR (1957 vs. 1956)",
       caption="Points are draws from posteior distribution") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))


```


Another way


```{r}

as_draws_df(m_pulmonary_overall)

```



#### 7.3 Compared to counterfactual

```{r}

overall_pulmonary_counterf <- calcuate_counterfactual(model_data = mdata1, model=m_pulmonary_overall, population_denominator = total_population)

overall_pulmonary_counterf %>%
  map(datatable)


```

Total pulmonary TB cases averted between 1958 and 1963

```{r}

overall_pulmonary_counterf$counter_post %>%
  summarise(across(c(cases_averted, cases_averted.lower, cases_averted.upper), sum))


```



### 8. Extra-pulmonary TB notifications

#### 8.1 Fit the

```{r, message=F, warning=FALSE}

m_extrap_overall <- brm(
  extrapulmonary_notifications ~ y_num + acf_period + acf_period:y_num +offset(log(total_population)),
                  data = mdata1,
                  family = poisson(),
                  seed = 1234,
                  chains = 4, cores = 4, 
                  prior = basic_prior,
                  save_pars = save_pars(all = TRUE),
                  backend = "cmdstanr",
                  warmup = 1000)

summary(m_extrap_overall)
plot(m_extrap_overall)
pp_check(m_extrap_overall, type='ecdf_overlay')

```


```{r}
plot_counterfactual(model_data = mdata1, model=m_extrap_overall, population_denominator = total_population, outcome=inc_ep_100k)
  
ggsave(here("figures/s7.png"), width=10)

```


#### 8.2 Summary of change

A. Percentage change in mortality, from 1956 to 1957 (i.e. immediate ACF effect)

```{r}

summarise_change(model_data=mdata1, model = m_extrap_overall, population_denominator = total_population) %>%
  map(datatable)

```



#### 8.3 Compared to counterfactual

```{r}

overall_ep_counterf <- calcuate_counterfactual(model_data = mdata1, model=m_extrap_overall, population_denominator = total_population)

overall_ep_counterf %>%
  map(datatable)


```

Total extra pulmonary TB cases averted between 1958 and 1963

```{r}

overall_ep_counterf $counter_post %>%
  summarise(across(c(cases_averted, cases_averted.lower, cases_averted.upper), sum))


```


### 9. Ward level model

#### 9.1 Fit the model

```{r}

mdata2 <- ward_inc %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(ward) %>%
  mutate(y_num = row_number()) %>%
  ungroup()



```

(Note the denominator without institutionalised people and "shipping"!)

```{r}
#weakly informative priors for multilevel model
basic_prior2 <- c(prior(normal(0, 1), class = Intercept),
                 prior(normal(0, 0.1), class = b),
                 prior(cauchy(0,5), class="sd"))


ggplot(data = tibble(x = seq(from = 0, to = 250, by = 10)),
       aes(x = x, y = dgamma(x, shape = 0.5, rate = 0.01))) +
  geom_area(color = "transparent", 
            fill = "#DE0D92") +
  scale_x_continuous(NULL) +
  scale_y_continuous(NULL, breaks = NULL) +
  coord_cartesian(xlim = c(0, 250)) +
  ggtitle(expression(brms~~gamma(0.5*", "*0.00001)~shape~prior))

winform_prior2 <- c(prior(normal(0, 1), class = Intercept),
                  prior(gamma(0.5, 0.01), class = shape),
                  prior(normal(0, 1), class = b),
                  prior(cauchy(0,5), class="sd"),
                  prior(lkj(2), class="cor"))
```


```{r}

m_pulmonary_ward_prior <- brm(
  cases ~ y_num + acf_period + acf_period:y_num + (1 + y_num + acf_period + acf_period:y_num | ward) + offset(log(population_without_inst_ship)),
                  data = mdata2,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4, 
                  prior = winform_prior2,
                  save_pars = save_pars(all = TRUE),
                  sample_prior = "only",
                  backend = "cmdstanr",
                  warmup = 1000)

conditional_effects(m_pulmonary_ward_prior)


```

Now fit the model with data

```{r}
m_pulmonary_ward <- brm(
  cases ~ y_num + acf_period + acf_period:y_num + (1 + y_num + acf_period + acf_period:y_num | ward) + offset(log(population_without_inst_ship)),
                  data = mdata2,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4, 
                  prior = winform_prior2,
                  backend = "cmdstanr")

summary(m_pulmonary_ward)
plot(m_pulmonary_ward)
pp_check(m_pulmonary_ward, type='ecdf_overlay')


```


```{r}

plot_counterfactual(model_data = mdata2, model=m_pulmonary_ward, outcome = inc_100k, population_denominator = population_without_inst_ship, grouping_var = ward, ward)
  
ggsave(here("figures/s3.png"), width=10, height=12)

```

#### 9.2 Summary of change

A. percentage increase in CNR, from 1956 to 1957 (i.e. immediate ACF effect)

```{r}

ward_change <- summarise_change(model_data = mdata2, model = m_pulmonary_ward, population_denominator = population_without_inst_ship, grouping_var=ward) 

ward_change %>%
  map(datatable)

```

As a supplementary figure

```{r}
  
ward_change$immediate_change %>%
  arrange(acf_inc100k_rr) %>%
  ggplot() +
  geom_pointrange(aes(y=acf_inc100k_rr, ymin=acf_inc100k_rr.lower, ymax=acf_inc100k_rr.upper, 
                      x=fct_reorder(ward, acf_inc100k_rr),
                      colour = acf_inc100k_rr)) +
  geom_hline(aes(yintercept=1), linetype=2) +
  coord_flip() +
  scale_colour_viridis_b(option = "D") +
  scale_y_continuous(limits = c(0.8,3.0)) +
  labs(x="",
       y="Relative posterior predicted case notification rate (per 100,000; 95% UI)\nACF (1957) vs. Before ACF (1956)") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))
  
ggsave(here("figures/s4.png"))

```


```{r}

ward_change$post_change %>%
  arrange(acf_inc100k_rr) %>%
  ggplot() +
  geom_pointrange(aes(y=acf_inc100k_rr, ymin=acf_inc100k_rr.lower, ymax=acf_inc100k_rr.upper, 
                      x=fct_reorder(ward, acf_inc100k_rr),
                      colour = acf_inc100k_rr)) +
  geom_hline(aes(yintercept=1), linetype=2) +
  coord_flip() +
  scale_colour_viridis_b(option = "D") +
  #scale_y_continuous(limits = c(0.8,3.0)) +
  labs(x="",
       y="Relative posterior predicted case notification rate (per 100,000; 95% UI)\nAfter ACF (1958) vs. Before ACF (1956)") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))

ggsave(here("figures/s5.png"))


```


percentage change = (final value - initial value) / initial value

```{r}

ward_change$slope_change %>%
  ggplot() +
  geom_pointrange(aes(y=pct_change_epred_overall , ymin=pct_change_lower_overall , ymax=pct_change_upper_overall ,
                      group=acf_period, colour=acf_period,
                      x = fct_reorder(ward, pct_change_epred_overall ))) +
  coord_flip() +
  scale_y_continuous(labels =percent) +
  scale_colour_manual(values = c("#DE0D92", "#4D6CFA")) +
  #scale_y_continuous(limits = c(0.8,3.0)) +
  labs(title="Intervention effect of mass miniature chest X-ray in Glasgow",
       subtitle="By municipal ward",
       x="",
       y="Mean annual rate of change in case notification rate (95% CrI)\n Before ACF (1950-1956) vs. after ACF (1958-1963)") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))

```

(Alternative figure - keep in for the minute)

Is there any correlation between immediate increase and a) post-intervention (1958) effect, and b) post intervention slope (1958-1963)

```{r}

ward_cors <- ward_impact_out %>%
  select(ward, immediate_effect = acf_inc100k_rr,
               immediate_effect_lower = acf_inc100k_rr.lower,
               immediate_effect_upper = acf_inc100k_rr.upper) %>%
  right_join(
    ward_pulm_impact2 %>% 
      select(ward, post_effect = acf_inc100k_rr,
               post_effect_lower = acf_inc100k_rr.lower,
               post_effect_upper = acf_inc100k_rr.upper)
  ) %>%
  right_join(
    ward_pulm_impact3 %>%
      filter(acf_period=="c. post-acf") %>%
      select(ward, slope_effect = pct_change_epred_annual,
             slope_effect_lower = pct_change_lower_annual,
             slope_effect_upper = pct_change_upper_annual)
  )

ward_cors %>%
  ggplot(aes(x=immediate_effect, xmin=immediate_effect_lower, xmax=immediate_effect_upper,
             y=post_effect, ymin=post_effect_lower, ymax=post_effect_upper,
             group = ward)) +
  geom_errorbar() +
  geom_errorbarh() +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_ggdist() +
  labs(title="Correlation between immediate ACF impact and post-ACF case notification rate",
       y="Relative case notification rate (1958 vs. 1956)\nPost intervention impact",
       x="Relative case notification rate (1957 vs. 1956)\nImmediate impact",
       caption="Points are municipal wards") +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))

ward_cors %>%
  ggplot(aes(x=immediate_effect, xmin=immediate_effect_lower, xmax=immediate_effect_upper,
             y=slope_effect, ymin=slope_effect_lower, ymax=slope_effect_upper,
             group = ward)) +
  geom_errorbar() +
  geom_errorbarh() +
  geom_smooth(method = "lm") +
  theme_ggdist() +
  labs(title="Correlation between immediate ACF impact and post-ACF mean rate of change",
       y="Post ACF mean annual rate of change in case notification rates",
       x="Relative case notification rate (1957 vs. 1956)\nImmediate impact",
       caption="Points are municipal wards") +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))



```

Try a different way with the full distribution of posteriors


```{r}

ward_immediate_draws <- mdata2 %>%
  select(year, year2, y_num, acf_period, population_without_inst_ship, cases, ward) %>%
  filter(year %in% c(1956,1957)) %>%
  add_epred_draws(m_pulmonary_ward) %>%
  mutate(inc_100k = .epred/population_without_inst_ship*100000) %>%
  group_by(ward, .draw) %>%
  summarise(pct_change_immediate = (last(inc_100k) - first(inc_100k))/first(inc_100k)) %>%
  arrange(ward) %>%
  ungroup()

ward_post_draws <- mdata2 %>%
  select(year, year2, y_num, acf_period, population_without_inst_ship, cases, ward) %>%
  filter(year %in% c(1956,1958)) %>%
  add_epred_draws(m_pulmonary_ward) %>%
  mutate(inc_100k = .epred/population_without_inst_ship*100000) %>%
  group_by(ward, .draw) %>%
  summarise(pct_change_post = (last(inc_100k) - first(inc_100k))/first(inc_100k)) %>%
  arrange(ward) %>%
  ungroup()


ward_slope_draws <- mdata2 %>%
  select(year, year2, y_num, acf_period, population_without_inst_ship, cases, ward) %>%
  filter(year %in% c(1950, 1956, 1958, 1963)) %>%
  add_epred_draws(m_pulmonary_ward) %>%
  mutate(inc_100k = .epred/population_without_inst_ship*100000) %>%
  ungroup() %>%
  mutate(n_years = case_when(acf_period=="a. pre-acf" ~ 7,
                             acf_period=="c. post-acf" ~ 6)) %>%
  group_by(ward, acf_period, .draw) %>%
  summarise(pct_change_slope = ((last(inc_100k) - first(inc_100k))/first(inc_100k))/n_years) %>%
  distinct() %>%
  pivot_wider(names_from = c(acf_period),
              values_from = pct_change_slope) %>%
  mutate(ratio_annual_slope = `c. post-acf` / `a. pre-acf`)
  




```


Correlation between immediate effect and post effect of ACF

```{r}


left_join(ward_immediate_draws, ward_post_draws) %>%
  ggplot(aes(x=pct_change_immediate, y=pct_change_post, group=ward)) +
  geom_hdr_points(size=0.1) +
  geom_smooth(method = "lm", se=FALSE, colour="black", linewidth=0.5) +
  stat_regline_equation(label.x = 0, label.y = 0.25, size=4) +
  scale_colour_scico_d(palette = "lipari", name = "Posterior probability") +
  scale_x_continuous(labels = percent) +
  scale_y_continuous(labels = percent) +
  labs(title="Correlation between immediate ACF impact and post-ACF case notification rate",
       y="Post intervention impact: ercentage change in CNR (1958 vs. 1956)",
       x="Immediate impact: percentage change in CNR (1957 vs. 1956)",
       caption="Points are draws from posteior distribution") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  facet_wrap(ward~.)


```

Correlation between immediate effect and change in slope

```{r}

left_join(ward_immediate_draws, ward_slope_draws) %>%
  ggplot(aes(x=pct_change_immediate, y=ratio_annual_slope, group=ward)) +
  geom_hdr_points(size=0.1) +
  geom_smooth(method = "lm", se=FALSE, colour="black", linewidth=0.5) +
  #stat_regline_equation(label.x = 0, label.y = 0.02, size=4) +
  scale_colour_scico_d(palette = "lipari", name = "Posterior probability") +
  scale_x_continuous(labels = percent) +
  scale_y_continuous(limits = c(0, 10)) +
  labs(title="Correlation between immediate ACF impact and post-ACF case notification rate",
       y="Post intervention impact: Percentage change in CNR (1958 vs. 1956)",
       x="Immediate impact: percentage change in CNR (1957 vs. 1956)",
       caption="Points are draws from posteior distribution") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  facet_wrap(ward~.)


```


Join these together with the overall estimates to make a single figure for showing impact

```{r}

f2_data <- 
  left_join(overall_immediate_draws, overall_post_draws) %>%
  left_join(overall_slope_draws) %>%
  mutate(level = "overall",
         ward = "Glasgow") %>%
  bind_rows(
    left_join(ward_immediate_draws, ward_post_draws) %>%
  left_join(ward_slope_draws) %>%
  mutate(level = "ward")
  )

f2a <- f2_data %>% 
  ggplot(aes(x=pct_change_immediate, y=pct_change_post, group=ward)) +
  geom_hdr_points(size=0.1) +
  geom_smooth(method = "lm", se=FALSE, colour="black", linewidth=0.5) +
  stat_regline_equation(label.x = 0, label.y = 0.12, size=4) +
  scale_colour_scico_d(palette = "lipari", name = "Posterior probability density") +
  scale_x_continuous(labels = percent) +
  scale_y_continuous(labels = percent) +
  labs(y="Post-intervention impact: Percentage change in CNR (1958 vs. 1956)",
       x="Immediate impact: percentage change in CNR (1957 vs. 1956)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  facet_wrap(fct_relevel(ward,
                         "Glasgow",
                         after=0)~., ncol = 5) + 
  guides(colour = guide_legend(override.aes = list(size=4)))

f2a

f2b <- f2_data %>% 
  ggplot(aes(x=pct_change_immediate, y=ratio_annual_slope, group=ward)) +
  geom_hdr_points(size=0.1) +
  geom_smooth(method = "lm", se=FALSE, colour="black", linewidth=0.5) +
  stat_regline_equation(label.x = 1.2, label.y = 12, size=4) +
  scale_x_continuous(labels = percent,
                     breaks = pretty_breaks(n = 4)) +
  scale_y_continuous(breaks = pretty_breaks(n = 4),
                limits = c(0,15)) +
  scale_fill_viridis_d(option="E") +
  labs(y="Post-intervention impact: Relative change in annual CNR slope (1958-1963 vs. 1950-1956)",
       x="Immediate impact: percentage change in CNR (1957 vs. 1956)",
       colour="Posterior probability density") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  facet_wrap(fct_relevel(ward,
                         "Glasgow",
                         after=0)~., ncol = 5) + 
  guides(colour = guide_legend(override.aes = list(size=4)))

f2b

(f2a / f2b) + plot_annotation(tag_levels = 'A')

ggsave(here("figures/f2.png"), height=18, width=10)

```

#### 9.3 Compared to counterfactual

```{r}

ward_counterf <- calcuate_counterfactual(model_data = mdata2, model=m_pulmonary_ward, population_denominator = population_without_inst_ship, grouping_var=ward)

ward_counterf %>%
  map(datatable)


```

Total pulmonary TB cases averted between 1958 and 1963

```{r}

ward_counterf$counter_post %>%
  summarise(across(c(cases_averted, cases_averted.lower, cases_averted.upper), sum), .by=ward)


```

### 10. Age-sex model

Fit the model

(Not rewritten the functions for this yet)

```{r}

mdata3 <- cases_by_age_sex %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  mutate(year2 = year+0.5) %>%
  group_by(age, sex) %>%
  mutate(y_num = row_number()) %>%
  ungroup()

winform_prior3 <- c(prior(normal(0, 1), class = Intercept),
                  #prior(gamma(0.5, 0.01), class = shape),
                  prior(normal(0, 1), class = b),
                  prior(cauchy(0,5), class="sd"),
                  prior(lkj(2), class="cor"))


m_age_sex <- brm(
  cases ~ y_num + (acf_period)*(age*sex) + (acf_period:y_num)*(age*sex),
                  data = mdata3,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4, 
                  prior = basic_prior,
                  backend = "cmdstanr")

summary(m_age_sex)
plot(m_age_sex)
pp_check(m_age_sex, type='ecdf_overlay')


```

Summarise posterior

```{r}

#posterior draws, and summarise
age_sex_summary <- mdata3 %>%
  select(year, year2, y_num, acf_period, age, sex) %>%
  add_epred_draws(m_age_sex) %>%
  group_by(year2, acf_period, age, sex) %>%
  mean_qi() %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention"))

#create the counterfactual (no intervention), and summarise
age_sex_counterfact <- 
  tibble(year = mdata3$year,
         year2 = mdata3$year2,
         y_num = mdata3$y_num,
         age = mdata3$age,
         sex = mdata3$sex,
         acf_period = factor("a. pre-acf")) %>%
  add_epred_draws(m_age_sex) %>%
  group_by(year2, acf_period, age, sex) %>%
  mean_qi() %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention")) %>%
  ungroup() %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  mutate(sex = case_when(sex== "M" ~ "Male",
                         sex== "F" ~ "Female")) 



age_sex_summary %>%
  ungroup() %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  mutate(sex = case_when(sex== "M" ~ "Male",
                         sex== "F" ~ "Female")) %>%
  ggplot() +
  geom_ribbon(aes(ymin=.epred.lower, ymax=.epred.upper, x=year2, group = acf_period, fill=acf_period), alpha=0.5) +
  geom_ribbon(data = age_sex_counterfact %>% filter(year>=1956), 
              aes(ymin=.epred.lower, ymax=.epred.upper, x=year2, fill="Counterfactual"), alpha=0.5) +
  geom_line(data = age_sex_counterfact %>% filter(year>=1956), 
              aes(y=.epred, x=year2, colour="Counterfactual")) +
  geom_line(aes(y=.epred, x=year2, group=acf_period,  colour=acf_period)) +
  geom_point(data = mdata3 %>%
  ungroup() %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  mutate(sex = case_when(sex== "M" ~ "Male",
                         sex== "F" ~ "Female")) , aes(y=cases, x=year2, shape=acf_period), size=2) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  ggh4x::facet_grid2(age~sex, scales = "free_y", independent = "y") +
  theme_ggdist() +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="") +
  scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="") +
  scale_shape_discrete(name="") +
  labs(
    x = "Year",
    y = "Case notifications (n)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA),
        title = element_text(size=14),
        axis.text = element_text(size=14),
        legend.text = element_text(size=12)) +
  guides(shape="none")
  
ggsave(here("figures/s6.png"), height=10)

```

Summary of impact of intervention

1. percentage increase in CNR, from 1956 to 1957 (i.e. immediate ACF effect)

```{r}

nd <- mdata3 %>%
  filter(year %in% c(1956:1957)) %>%
  select(acf_period, y_num, age, sex)


age_sex_impact_out <- 
  add_epred_draws(m_age_sex,
                newdata=nd) %>%
  ungroup() %>%
  select(acf_period, .epred, age, sex) %>%
  pivot_wider(names_from = acf_period,
              values_from = .epred,
              values_fn = list) %>%
  unnest() %>%
  rename(pre_epred = 3,
         post_epred = 4) %>%
  mutate(acf_diff = post_epred-pre_epred,
         acf_rr = post_epred/pre_epred) %>%
  group_by(age, sex) %>%
  mean_qi(acf_diff, acf_rr) 

age_sex_impact_out %>%
  mutate_if(is.double, ~ scales::number(x = ., accuracy = 0.01, big.mark = ",")) %>%
  datatable()
  
f3a <- age_sex_impact_out %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_pointrange(aes(y=acf_rr, ymin=acf_rr.lower, ymax=acf_rr.upper, group=sex, 
                      x=age,
                      colour = sex),
                  position = position_dodge(width = 0.25)) +
  geom_hline(aes(yintercept=1), linetype=2) +
  scale_colour_manual(values = c("purple", "darkorange"), name="") +
  labs(x="",
       y="Relative notifications (95% UI)\nACF (1957) vs. Before ACF (1956)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))
  
  

```


2. Change from pre-ACF period (1956), to first year post-ACF (1958)


```{r}

nd <- mdata3 %>%
  filter(year %in% c(1956,1958)) %>%
  select(acf_period, y_num, age, sex)

#Do it with calculating incidence, then sumamrising.
age_sex_impact2 <-add_epred_draws(m_age_sex,
                newdata=nd) %>%
  ungroup() %>%
  select(acf_period, .epred, age, sex) %>%
  pivot_wider(names_from = acf_period,
              values_from = .epred,
              values_fn = list) %>%
  unnest() %>%
  rename(pre_epred = 3,
        post_epred = 4) %>%
  mutate(acf_diff = post_epred-pre_epred,
         acf_rr = post_epred/pre_epred) %>%
  group_by(age, sex) %>%
  mean_qi(acf_diff, acf_rr) 

age_sex_impact2 %>%
  mutate_if(is.double, ~ scales::number(x = ., accuracy = 0.01, big.mark = ",")) %>%
  datatable()

f3b <- age_sex_impact2 %>%  
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_pointrange(aes(y=acf_rr, ymin=acf_rr.lower, ymax=acf_rr.upper, group=sex, 
                      x=age,
                      colour = sex),
                  position = position_dodge(width = 0.25)) +
  geom_hline(aes(yintercept=1), linetype=2) +
  scale_colour_manual(values = c("purple", "darkorange"), name="") +
  labs(x="",
       y="Relative notifications (95% UI)\nACF (1958) vs. Before ACF (1956)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))


```

3. Change in slope (i.e. difference in mean annual case notification rate pre-Intervention vs. post-intervention, by ward)

```{r}

age_sex_impact3 <- mdata3 %>%
  select(year, year2, y_num, acf_period, cases, age, sex) %>%
  filter(year!=1957) %>%
  add_epred_draws(m_age_sex) %>%
  group_by(year, age, sex, acf_period) %>%
  mean_qi(.epred) %>%
  ungroup() %>%
  mutate(n_years = length(year), .by=acf_period) %>%
  summarise(pct_change_epred_overall = (((last(.epred) - first(.epred))/first(.epred))),
            pct_change_lower_overall = (((last(.lower) - first(.lower))/first(.lower))),
            pct_change_upper_overall = (((last(.upper) - first(.upper))/first(.upper))),
    
            pct_change_epred_annual = (((last(.epred) - first(.epred))/first(.epred))/n_years),
            pct_change_lower_annual = (((last(.lower) - first(.lower))/first(.lower))/n_years),
            pct_change_upper_annual = (((last(.upper) - first(.upper))/first(.upper))/n_years),
            .by = c(acf_period, age, sex)) %>%
  distinct()


age_sex_impact3 %>%
  mutate_if(is.double, percent) %>%
  datatable()

f3c <- age_sex_impact3 %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
    geom_pointrange(aes(y=pct_change_epred_annual, ymin=pct_change_lower_annual, ymax=pct_change_upper_annual, group=acf_period, 
                      x=age,
                      colour = acf_period), size=0.1) +
  scale_y_continuous(labels =percent) +
  facet_grid(.~sex) +
  coord_flip() +
  scale_colour_manual(values = c("#DE0D92", "#4D6CFA")) +
  labs(x="",
       y="Mean annual rate of change in case notification rate (95% UI)\n Before ACF (1950-1956) vs. after ACF (1958-1963)",
       colour="") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))

f3c

```

Join together for Figure 2.

```{r}

(f3a / f3b / f3c) + plot_annotation(tag_levels = "A")

ggsave(here("figures/f3.png"), height=12)


```



Is there any correlation between immediate increase and a) post-intervention (1958) effect, and b) post intervention slope (1958-1963)

```{r}

age_sex_cors <- age_sex_impact_out %>%
  select(age, sex, immediate_effect = acf_rr,
               immediate_effect_lower = acf_rr.lower,
               immediate_effect_upper = acf_rr.upper) %>%
  right_join(
    age_sex_impact2 %>% 
      select(age, sex, post_effect = acf_rr,
               post_effect_lower = acf_rr.lower,
               post_effect_upper = acf_rr.upper)
  ) %>%
  right_join(
    age_sex_impact3 %>%
      filter(acf_period=="c. post-acf") %>%
      select(age, sex, slope_effect = pct_change_epred,
             slope_effect_lower = pct_change_lower,
             slope_effect_upper = pct_change_upper)
  )

age_sex_cors %>%
  mutate(age_sex = paste0(sex, age, sep = " ")) %>%
  ggplot(aes(x=immediate_effect, xmin=immediate_effect_lower, xmax=immediate_effect_upper,
             y=post_effect, ymin=post_effect_lower, ymax=post_effect_upper,
             group = age_sex, colour=age_sex)) +
  geom_errorbar() +
  geom_errorbarh() +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_ggdist() +
  labs(title="Correlation between immediate ACF impact and post-ACF case notifications",
       y="Relative case notification rate (1958 vs. 1956)\nPost intervention impact",
       x="Relative case notification rate (1957 vs. 1956)\nImmediate impact",
       caption="Points are age-sex groups") +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))

age_sex_cors %>%
  mutate(age_sex = paste0(sex, age, sep = " ")) %>%
  ggplot(aes(x=immediate_effect, xmin=immediate_effect_lower, xmax=immediate_effect_upper,
             y=slope_effect, ymin=slope_effect_lower, ymax=slope_effect_upper,
             group = age_sex, colour=age_sex)) +
  geom_errorbar() +
  geom_errorbarh() +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_ggdist() +
  labs(title="Correlation between immediate ACF impact and post-ACF mean rate of change",
       y="Post ACF mean annual rate of change in case notification rates",
       x="Relative case notification rate (1957 vs. 1956)\nImmediate impact",
       caption="Points are municipalwards") +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))



```




### 11. Division-level model

(Very much a work in progress!)

```{r}

mdata3 <- division_inc %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(division) %>%
  mutate(y_num = row_number()) %>%
  ungroup()



```



```{r}

m2_pulmonary_division <- brm(
  cases ~ y_num + acf_period + acf_period:y_num + (1 + y_num + acf_period + acf_period:y_num | division / ward) + offset(log(population_without_inst_ship)),
                  data = mdata2,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4, 
                  prior = inform_prior2,
                  save_pars = save_pars(all = TRUE),
                  backend = "cmdstanr",
                  warmup = 1000)

summary(m2_pulmonary_division)
plot(m2_pulmonary_division)
pp_check(m2_pulmonary_division, type='ecdf_overlay')



```

```{r}

#posterior draws, and summarise
ward_pulmonary_summary2 <- mdata2 %>%
  select(year, year2, y_num, acf_period, population_without_inst_ship, inc_100k, ward) %>%
  add_epred_draws(m2_pulmonary_ward) %>%
  group_by(year2, acf_period, ward) %>%
  mean_qi() %>%
  mutate(.epred_inc = .epred/population_without_inst_ship*100000,
         .epred_inc.lower = .epred.lower/population_without_inst_ship*100000,
         .epred_inc.upper = .epred.upper/population_without_inst_ship*100000) %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention"))

#create the counterfactual (no intervention), and summarise
ward_pulmonary_counterfact2 <- 
  tibble(year = mdata2$year,
         year2 = mdata2$year2,
         y_num = mdata2$y_num,
         ward = mdata2$ward,
         acf_period = factor("a. pre-acf"),
         population_without_inst_ship = mdata2$population_without_inst_ship) %>%
  add_epred_draws(m2_pulmonary_ward) %>%
  group_by(year2, acf_period, ward) %>%
  mean_qi() %>%
  mutate(.epred_inc = .epred/population_without_inst_ship*100000,
         .epred_inc.lower = .epred.lower/population_without_inst_ship*100000,
         .epred_inc.upper = .epred.upper/population_without_inst_ship*100000) %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention"))





```

```{r}

ward_pulmonary_summary2 %>%
  droplevels() %>%
  ggplot() +
  geom_ribbon(aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, group = acf_period, fill=acf_period), alpha=0.5) +
  geom_ribbon(data = ward_pulmonary_counterfact2 %>% filter(year>=1956), 
              aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, fill="Counterfactual"), alpha=0.5) +
  geom_line(data = ward_pulmonary_counterfact2 %>% filter(year>=1956), 
              aes(y=.epred_inc, x=year2, colour="Counterfactual")) +
  geom_line(aes(y=.epred_inc, x=year2, group=acf_period,  colour=acf_period)) +
  geom_point(data = mdata2 , aes(y=inc_100k, x=year2, shape=acf_period), size=2) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  facet_wrap(ward~.) +
  theme_ggdist() +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="") +
  scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="") +
  scale_shape_discrete(name="") +
  labs(
    title = "Glasgow Corporation: pulmonary tuberculosis case notification rate, by Ward",
    subtitle = "Empirical, and model-predicted: 1950 to 1963",
    x = "Year",
    y = "Case notification rate (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nModel estimates from Bayesian interrupted time series regression model"
  ) +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA),
        title = element_text(size=14),
        axis.text = element_text(size=14),
        legend.text = element_text(size=12)) +
  guides(shape="none")
  


```
